SYSTEMS AND METHODS FOR DETERMINING THE STATUS OF A TURN LANE TRAFFIC LIGHT

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane. 1. A traffic light detection system for a vehicle , the system comprising:at least one image capture device configured to acquire at least one image of an area forward of the vehicle, the area including a traffic lamp fixture having at least one traffic light;a data interface; and receive the at least one acquired image via the data interface;', 'determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane;', 'perform image processing on the at least one image to determine whether an arrow exists in the at least one image within pixels of the image representative of the traffic light;', 'determine, based on the at least one image, a status of the traffic light; and', 'cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane., 'at least one processing device configured to2. The traffic light detection system of claim 1 , wherein the at least one indicator of vehicle position includes a lane marker recognized based on analysis of the at least one ...

Constraint relaxation in a navigational system

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; ...

Object detection using candidate object alignment

Classification of an object in the field of view of a camera. A processor is configured to capture multiple image frames from the camera. A candidate image is detected in the image frames. Alignment of the candidate image is determined relative to at least one previously known training image by inputting the candidate image into a trained alignment classifier and outputting one or more alignment variables therefrom associated with the candidate image. The candidate image and the alignment variable(s) are input into a trained object classifier. The object is classified responsive to the alignment variable(s).

CONSTRAINT AUGMENTATION IN A NAVIGATIONAL SYSTEM

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; determine a first predefined navigational constraint implicated by at least one aspect of the navigational state; identify, based on analysis of the plurality of images, a presence of at least one navigational constraint augmentation factor; determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor; determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action. 175.-. (canceled)76. A navigation system for a host vehicle , the system comprising: receive, from a camera, a plurality of images representative of an environment of the host vehicle;', 'analyze the plurality of images to identify a navigational state associated with the host vehicle;', 'determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;', 'identify, based on analysis of the plurality of images, a presence of at least one navigational constraint augmentation factor;', 'determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic augmented with respect to the first navigational constraint;', 'determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and', 'cause ...

Adaptive road model manager

Systems and methods are provided for interacting with a plurality of autonomous vehicles. In one implementation, a system may include at least one processor. The at least one processor may be programmed to receive from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver, analyze the navigational situation information, determine, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition, and update the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition.

Autonomous vehicle tail alignment navigation

A system for navigating an autonomous vehicle along a road segment is disclosed. The system may have at least one processor. The processor may be programmed to receive from an image capture device, images representative of an environment of the autonomous vehicle. The processor may also be programmed to determine a travelled trajectory along the road segment based on analysis of the images. Further, the processor may be programmed to determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images. The processor may also be programmed to determine a heading direction based on the determined traveled trajectory. In addition, the processor may be programmed to determine a steering direction, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

Navigating road junctions

A system for autonomously navigating a vehicle through a road junction may include at least one processor programmed to receive from an image capture device at least one image representative of an environment of the vehicle. The processor may also be configured to analyze the image to identify two or more landmarks located in the environment of the vehicle and to determine, for each of the two or more landmarks, a directional indicator relative to the vehicle. The processor may be configured to determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators a heading for the vehicle based on the directional indicators for the two or more landmarks. The processor may be configured to determine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle.

Systems and methods for detecting traffic signs

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold.

Systems and methods for detecting traffic signs

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold.

Detecting and recognizing traffic signs

A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

Systems and methods for determining the status of a turn lane traffic light

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane.

FULLY ALIGNED JUNCTIONS

Systems and methods for creating maps used in navigating autonomous vehicles are disclosed. In one implementation at least one processor is programmed to receive drive information from each of a plurality of vehicles that traverse different entrance-exit combinations of a road junction; for each of the entrance-exit combinations, align three-dimensional feature points in the drive information to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation, the sparse map including a target trajectory associated with each of the entrance-exit combinations. 150-. (canceled)51. A sparse map generator system for creating maps used in navigating autonomous or partially autonomous vehicles , comprising: receive drive information from each of a plurality of vehicles that traverse a road junction, the road junction including a plurality of entrances and one or more exits associated with each one of the plurality of entrances; wherein the plurality of vehicles are each associated with a vehicle group from a plurality of vehicle groups, each vehicle group including one or more vehicles that traverse a different entrance-exit combination associated with the road junction; and wherein the drive information from each of the plurality of vehicles includes three-dimensional feature points associated with objects detected by analyzing images captured as a particular vehicle traversed a particular entrance-exit combination of the road junction;', 'for each of the entrance-exit combinations, align the three-dimensional feature points received ...

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Systems and methods for determining the status of a turn lane traffic light

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane.

CONSTRAINT RELAXATION IN A NAVIGATIONAL SYSTEM

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; determine a first predefined navigational constraint implicated by at least one aspect of the navigational state; identify, based on analysis of the plurality of images, a presence of at least one navigational constraint relaxation factor; determine a second navigational constraint based on identification of the at least one navigational constraint relaxation factor; determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action. 150.-. (canceled)51. A navigation system for a host vehicle , the system comprising: receive, from a camera, a plurality of images representative of an environment of the host vehicle;', 'analyze the plurality of images to identify a navigational state associated with the host vehicle;', 'determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;', 'identify, based on analysis of the plurality of images, a presence of at least one navigational constraint relaxation factor;', 'determine a second navigational constraint based on identification of the at least one navigational constraint relaxation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic relaxed with respect to the first navigational constraint;', 'determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and', 'cause at least ...

SYSTEMS AND METHODS FOR NAVIGATING LANE MERGES AND LANE SPLITS

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

SYSTEMS AND METHODS FOR NAVIGATING LANE MERGES AND LANE SPLITS

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Detecting and recognizing traffic signs

A computerized system mountable on a moving vehicle. The computerized system includes a camera. The camera captures in real time image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The image processor is programmed for performing traffic sign recognition and for performing another driver assistance function. Information is exchanged between the traffic sign recognition and the other driver assistance function.

SYSTEMS AND METHODS FOR DETECTING TRAFFIC SIGNS

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold. 130-. (canceled)31. A traffic sign detection system for a vehicle , comprising: generate a plurality of images having different sizes based on an image of an environment ahead of the vehicle that includes a traffic sign;', 'convolve the plurality of images with a template image;', 'compare one or more pixel values of the convolved images to a predetermined threshold;', 'select local maxima of pixel values within local regions of the convolved images as attention candidates, the local maxima being greater than the predetermined threshold; and', 'detect the traffic sign based on the attention candidates., 'at least one processing device programmed to32. The system of claim 31 , wherein the at least one processing device is further programmed to transform the image of the environment.33. The system of claim 32 , wherein transforming the image of the environment comprises calculating at least one pair of gradients for each pixel in the image.34. The system of claim 33 , wherein transforming the image further comprises calculating an angle from the at least one pair of gradients.35. The system ...

Constraint augmentation in a navigational system

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; determine a first predefined navigational constraint implicated by at least one aspect of the navigational state; identify, based on analysis of the plurality of images, a presence of at least one navigational constraint augmentation factor; determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor; determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

NAVIGATING ROAD JUNCTIONS

A system for autonomously navigating a vehicle through a road junction may include at least one processor programmed to receive from an image capture device at least one image representative of an environment of the vehicle. The processor may also be configured to analyze the image to identify two or more landmarks located in the environment of the vehicle and to determine, for each of the two or more landmarks, a directional indicator relative to the vehicle. The processor may be configured to determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators a heading for the vehicle based on the directional indicators for the two or more landmarks. The processor may be configured to determine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle. 128.-. (canceled)29. A system for autonomously navigating a vehicle through a road junction , the system comprising:at least one processor programmed to:receive from an image capture device at least one image representative of an environment of the vehicle;analyze the at least one image to identify two or more landmarks located in the environment of the vehicle;determine, for each of the two or more landmarks, a directional indicator relative to the vehicle;determine a current location of the vehicle relative to the road junction based on an intersection of the directional indicators for the two or more landmarks;determine a heading for the vehicle based on the directional indicators for the two or more landmarks; anddetermine a steering angle for the vehicle by comparing the vehicle heading with a predetermined road model trajectory at the current location of the vehicle.30. The system of claim 29 , wherein the predetermined road model trajectory includes a three-dimensional polynomial representation of a target trajectory along the road segment.31. The system of ...

SYSTEMS AND METHODS FOR NAVIGATING LANE MERGES AND LANE SPLITS

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle. 1. A system for navigating a host vehicle , the system comprising:at least one processing device comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processing device to:receive a plurality of images acquired by an image capture device, the plurality of images being representative of an environment of the host vehicle;analyze at least one of the plurality of images to identify a target vehicle traveling in a lane of a road;analyze at least one of the plurality of images to identify at least one lane mark associated with the lane in which the target vehicle is traveling;determine at least one characteristic of the target vehicle based on at least one of the plurality of images, wherein the at least one determined characteristic of the target vehicle includes a detected motion of the target vehicle relative to the at least one identified lane mark; anddetermine a navigational action for the host vehicle based on the determined ...

Object detection using candidate object alignment

Classification of an object in the field of view of a camera. A processor is configured to capture multiple image frames from the camera. A candidate image is detected in the image frames. Alignment of the candidate image is determined relative to at least one previously known training image by inputting the candidate image into a trained alignment classifier and outputting one or more alignment variables therefrom associated with the candidate image. The candidate image and the alignment variable(s) are input into a trained object classifier. The object is classified responsive to the alignment variable(s).

DETECTING AND RECOGNIZING TRAFFIC SIGNS

A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

NAVIGATION BASED ON EXPECTED LANDMARK LOCATION

A system for autonomously navigating a vehicle along a road segment may be based on a predetermined landmark location. The system may include at least one processor programmed to receive from a camera, at least one image representative of an environment of the vehicle, and determine a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image. The at least one processor may be further programmed to identify a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera, and determine a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark. The at least one processor may also be programmed to determine an autonomous navigational response for the vehicle based on the determined current distance. 128-. (canceled)29. A system for autonomously navigating a vehicle along a road segment based on a predetermined landmark location , the system comprising:at least one processor programmed to:receive from a camera, at least one image representative of an environment of the vehicle;determine a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image;identify a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera;determine a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark; anddetermine an autonomous navigational response for the vehicle based on the determined current distance.30. The system of claim 29 , wherein the ...

Navigation based on expected landmark location

A system for autonomously navigating a vehicle along a road segment may be based on a predetermined landmark location. The system may include at least one processor programmed to receive from a camera, at least one image representative of an environment of the vehicle, and determine a position of the vehicle along a predetermined road model trajectory associated with the road segment based, at least in part, on information associated with the at least one image. The at least one processor may be further programmed to identify a recognized landmark forward of the vehicle based on the determined position, wherein the recognized landmark is beyond a sight range of the camera, and determine a current distance between the vehicle and the recognized landmark by comparing the determined position of the vehicle with a predetermined position of the recognized landmark. The at least one processor may also be programmed to determine an autonomous navigational response for the vehicle based on the determined current distance.

Systems and methods for identifying relevant traffic lights

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a system includes at least one image capture device configured to acquire at least one image of an area forward of the vehicle. The area includes a plurality of traffic lamp fixtures each including at least one traffic light. The system further includes at least one processing device configured to use at least one indicator of vehicle position, as determined from the at least one acquired image, to determine a relevance to the vehicle of each of the plurality of traffic lamp fixtures. The at least one processing device may determine, based on the at least one acquired image, a status of a traffic light included in at least one traffic lamp fixture determined to be relevant to the vehicle, and cause a system response based on the determined status.

Determining lane assignment based on recognized landmark location

Systems and methods are provided for determining a lane assignment for an autonomous vehicle along a road segment. In one implementation, at least one image representative of an environment of the vehicle is received from a camera. The at least one image may be analyzed to identify at least one recognized landmark, and an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark may be determined. Moreover, a lane assignment of the vehicle along the road segment may be determined based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark.

DETERMINING LANE ASSIGNMENT BASED ON RECOGNIZED LANDMARK LOCATION

Systems and methods are provided for determining a lane assignment for an autonomous vehicle along a road segment. In one implementation, at least one image representative of an environment of the vehicle is received from a camera. The at least one image may be analyzed to identify at least one recognized landmark, and an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark may be determined. Moreover, a lane assignment of the vehicle along the road segment may be determined based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark. 128.-. (canceled)29. A system for determining a lane assignment for an autonomous vehicle along a road segment , the system comprising: receive from a camera at least one image representative of an environment of the vehicle;', 'analyze the at least one image to identify at least one recognized landmark;', 'determine an indicator of a lateral offset distance between the vehicle and the at least one recognized landmark; and', 'determine a lane assignment of the vehicle along the road segment based on the indicator of the lateral offset distance between the vehicle and the at least one recognized landmark., 'at least one processor programmed to30. The system of claim 29 , wherein the environment of the vehicle includes the road segment claim 29 , a number of lanes claim 29 , and the at least one recognized landmark.31. The system of claim 29 , wherein the at least one recognized landmark includes at least one of a traffic sign claim 29 , an arrow marking claim 29 , a lane marking claim 29 , a dashed lane marking claim 29 , a traffic light claim 29 , a stop line claim 29 , a directional sign claim 29 , a reflector claim 29 , a landmark beacon claim 29 , or a lamppost.32. The system of claim 29 , wherein the at least one recognized landmark includes a sign for a business.33. The system of claim 29 , wherein the lateral offset distance ...

Constraint augmentation in a navigational system

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

CONSTRAINT RELAXATION IN A NAVIGATIONAL SYSTEM

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle. 150.-. (canceled)51. A navigation system for a host vehicle , the system comprising: receive, from a camera, a plurality of images representative of an environment of the host vehicle;', 'analyze the plurality of images to identify a navigational state associated with the host vehicle;', 'determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;', 'identify, based on analysis of the plurality of images, a presence of at least one navigational constraint relaxation factor;', 'determine a second navigational constraint based on identification of the at least one navigational constraint relaxation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic relaxed with respect to the first ...

Constraint relaxation in a navigational system

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle, analyze the plurality of images to identify a navigational state associated with the host vehicle, provide the navigational state to a trained navigational system, receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state, analyze the desired navigational action relative to one or more predefined navigational constraints, determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints, ...

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Systems and methods for detecting traffic signs

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold.

DETECTING AND RECOGNIZING TRAFFIC SIGNS

A computerized system mountable on a moving vehicle. The computerized system includes a camera. The camera captures in real time image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The image processor is programmed for performing traffic sign recognition and for performing another driver assistance function. Information is exchanged between the traffic sign recognition and the other driver assistance function. 1. A method for performing driver assistance by means of a computerized system mountable on a moving vehicle , the computerized system including a camera , wherein the camera captures in real time a plurality of image frames of the environment in the field of view of the camera and transfers the image frames to an image processor , the method comprising:programming the image processor for performing traffic sign recognition and for performing another driver assistance function; andexchanging information between the traffic sign recognition and the other driver assistance function.2. The method of claim 1 , wherein said other driver assistance function verifies the presence of a lead vehicle claim 1 , the method further comprising:upon detecting an image suspected to be of a traffic sign,associating said image to be an object attached to the lead vehicle when said image scales with said lead vehicle.3. The method of claim 1 , wherein said other driver assistance function determines a lane in which the moving vehicle is traveling claim 1 , the method further comprising:upon detecting an image suspected to be of a traffic sign associating said image as an image of a traffic sign for said other lane of traffic.4. The method of claim 1 , wherein said other driver assistance function estimates from the image frames an ego-motion including a translation in the forward direction and a rotation of the moving vehicle claim 1 , the method further comprising:upon detecting an image suspected to be of a ...

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

CONSTRAINT AUGMENTATION IN A NAVIGATIONAL SYSTEM

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle. 175.-. (canceled)76. A navigation system for a host vehicle , the system comprising: receive, from a camera, a plurality of images representative of an environment of the host vehicle;', 'analyze the plurality of images to identify a navigational state associated with the host vehicle;', 'determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;', 'identify, based on analysis of the plurality of images, a presence of at least one navigational constraint augmentation factor;', 'determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic augmented with respect to the ...

SYSTEMS AND METHODS FOR IDENTIFYING RELEVANT TRAFFIC LIGHTS

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a system includes at least one image capture device configured to acquire at least one image of an area forward of the vehicle. The area includes a plurality of traffic lamp fixtures each including at least one traffic light. The system further includes at least one processing device configured to use at least one indicator of vehicle position, as determined from the at least one acquired image, to determine a relevance to the vehicle of each of the plurality of traffic lamp fixtures. The at least one processing device may determine, based on the at least one acquired image, a status of a traffic light included in at least one traffic lamp fixture determined to be relevant to the vehicle, and cause a system response based on the determined status.

Systems and methods for determining the status and details of a traffic light

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane.

Updating road navigation model using non-semantic road feature points

A navigation system may include a processor programmed to analyze a first image to identify a non-semantic road feature; identify a first image location, in the first image, of one point associated with the non-semantic road feature; analyze a second image to identify a representation of the non-semantic road feature in the second image; identify a second image location, in the second image, of the one point associated with the non-semantic road feature; determine, based on a difference between the first and second image locations and based on motion information for a vehicle between a capture of the first image and a capture of the second image, three-dimensional coordinates for the one point associated with the non-semantic road feature; and send the three-dimensional coordinates for the one point associated with the non-semantic road feature to a server for use in updating a road navigation model.

Autonomous vehicle tail alignment navigation

A system for navigating an autonomous vehicle along a road segment is disclosed. The system may have at least one processor. The processor may be programmed to receive from an image capture device, images representative of an environment of the autonomous vehicle. The processor may also be programmed to determine a travelled trajectory along the road segment based on analysis of the images. Further, the processor may be programmed to determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images. The processor may also be programmed to determine a heading direction based on the determined traveled trajectory. In addition, the processor may be programmed to determine a steering direction, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

Adaptive road model manager

Systems and methods are provided for interacting with a plurality of autonomous vehicles. In one implementation, a system may include at least one processor. The at least one processor may be programmed to receive from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver, analyze the navigational situation information, determine, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition, and update the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition.

Systems and methods for detecting traffic signs

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold.

Low Resolution Traffic Light Candidate Identification Followed by High Resolution Candidate Analysis

Systems and methods are provided for vehicle navigation. The systems and methods may detect traffic lights. For example, one or more traffic lights may be detected using detection-redundant camera detection paths, a fusion of information from a traffic light transmitter and one or more cameras, based on contrast enhancement for night images, and based on low resolution traffic light candidate identification followed by high resolution candidate analysis. Additionally, the systems and methods may navigation based on a worst time to red estimation.

Redundant Camera Detection Paths

Systems and methods are provided for vehicle navigation. The systems and methods may detect traffic lights. For example, one or more traffic lights may be detected using detection-redundant camera detection paths, a fusion of information from a traffic light transmitter and one or more cameras, based on contrast enhancement for night images, and based on low resolution traffic light candidate identification followed by high resolution candidate analysis. Additionally, the systems and methods may navigation based on a worst time to red estimation. 1. A navigation system for a vehicle , the navigation system comprising: receive, from a first camera of the vehicle, a first image captured from an environment of the vehicle;', 'receive, from a second camera of the vehicle, a second image captured from the environment of the vehicle;', 'analyze the first image to generate a first detection result, wherein the first detection result includes an identification of a traffic light and a state of the traffic light;', 'analyze the second image to generate a second detection result, wherein the second detection result includes an identification of the traffic light and a state of the traffic light;', 'compare the first detection result and the second detection result to determine a third detection result, wherein the third detection result includes a confirmed state of the traffic light;', 'determine a navigational action for the vehicle based on the confirmed state of the traffic light; and', 'cause the vehicle to implement the navigational action., 'at least one processor comprising circuitry and having access to a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to2. The system of claim 1 , wherein the state of the traffic light included in the first detection result differs from the state of the traffic light included in the second detection result.3. The system of claim 1 , wherein execution of the ...

SYSTEMS AND METHODS FOR DETERMINING THE STATUS OF A TURN LANE TRAFFIC LIGHT

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane. 1. A traffic light detection system for a vehicle , the system comprising:at least one image capture device configured to acquire at least one image of an area forward of the vehicle, the area including a traffic lamp fixture having at least one traffic light;a data interface; and receive the at least one acquired image via the data interface;', 'determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane;', 'perform image processing on the at least one image to determine whether an arrow exists in the at least one image within pixels of the image representative of the traffic light;', 'determine, based on the at least one image, a status of the traffic light; and', 'cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane., 'at least one processing device configured to2. The traffic light detection system of claim 1 , wherein the at least one indicator of vehicle position includes a lane marker recognized based on analysis of the at least one ...

Systems and methods for determining the status and details of a traffic light

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image of an area forward of the vehicle via a data interface, with the area including at least one traffic lamp fixture having at least one traffic light. The processing device(s) determine, based on at least one indicator of vehicle position, whether the vehicle is in a turn lane. Also, the processing device(s) process the received image(s) to determine the status of the traffic light, including whether the traffic light includes an arrow. Further, the system may cause a system response based on the determination of the status of the traffic light, whether the traffic light includes an arrow, and whether the vehicle is in a turn lane.

SYSTEMS AND METHODS FOR DETECTING TRAFFIC SIGNS

Systems and methods are provided for detecting traffic signs. In one implementation, a traffic sign detection system for a vehicle include at least one image capture device configured to acquire at least one image of a scene including a traffic sign ahead of the vehicle. The traffic sign detection system also includes a data interface and at least one processing device programmed to receive the at least one image via the data interface, transform the at least one image, sample the transformed at least one image to generate a plurality of images having different sizes, convolve each of the plurality of images with a template image, compare each pixel value of each convolved image to a predetermined threshold, and select local maxima of pixel values within local regions of each convolved image as attention candidates, the local maxima being greater than the predetermined threshold.

Detecting and recognizing traffic signs

A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

Systems and methods for lane end recognition

Detecting and recognizing traffic signs

A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

Sparse map for autonomous vehicle navigation

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters. The sparse map has a data density of no more than l megabyte per kilometer.

Sparse map for autonomous vehicle navigation

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium 5 includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters. The sparse map has a data density of no more than 1 megabyte per kilometer. WO 2016/130719 PCT/US2016/017411 Map ---- - ------ ------- Database 180 128 190 -- -- -- - -- -- -- -- -- -- - -- - - 1 110 Application Image Processor Processor Memory Memory Sensors User Interface

sparse map for autonomous vehicle navigation

são revelados sistemas e métodos para construir, usar e atualizar um mapa esparso para a navegação de veículos autônomos. em uma implementação, um meio não transitório legível por computador inclui um mapa esparso para a navegação de veículos autônomos ao longo de um segmento de uma estrada. o mapa esparso inclui uma representação polinomial de uma trajetória almejada para o veículo autônomo ao longo do segmento de estrada e vários pontos de referência predeterminados associados ao segmento de estrada, em que os vários pontos de referência predeterminados são espaçados em ao menos 50 metros. o mapa esparso tem uma densidade de dados de não mais que 1 megabyte por quilômetro. Systems and methods for building, using and updating a sparse map for autonomous vehicle navigation are revealed. In one implementation, a computer readable non-transient means includes a sparse map for navigating autonomous vehicles along a segment of a road. the sparse map includes a polynomial representation of a desired trajectory for the autonomous vehicle along the road segment and various predetermined landmarks associated with the road segment, wherein the various predetermined landmarks are spaced at least 50 meters apart. The sparse map has a data density of no more than 1 megabyte per kilometer.

Navigation based on vehicle activity

Trained navigational system with imposed constraints

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

Systems and methods for lane end recognition

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a lane ending detection system is provided for a vehicle. One or more processing devices associated with the system receive at least one image via a data interface. The device(s) extract lane ending information from the road sign(s) included in the image data and determine, based on at least one indicator of position of the vehicle, a distance from the vehicle to one or more lane constraints associated with the current lane. The processing device(s) determine, based on the lane ending information and the vehicle position, whether a current lane in which the vehicle is traveling is ending. Further, the system may cause the vehicle to change lanes if the lane in which the vehicle is traveling is ending.

Navigation based on vehicle activity

The present disclosure relates to systems and methods for host vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a first flow of traffic and a second flow of traffic; determine a presence of at least one navigational state characteristic indicative of an alternating merging of the first flow of traffic and the second flow of traffic into a merged lane; cause at least a first navigational change to allow one target vehicle from the first flow of traffic to proceed ahead of the host vehicle; and cause at least a second navigational change to cause the host vehicle to follow the target vehicle into the merged lane.

Systems and methods for vehicle navigation

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

Detecting and recognizing traffic signs

A method for detecting and identifying a traffic sign in a computerized system mounted on a moving vehicle. The system includes a camera mounted on the moving vehicle. The camera captures in real time multiple image frames of the environment in the field of view of the camera and transfers the image frames to an image processor. The processor is programmed for performing the detection of the traffic sign and for performing another driver assistance function. The image frames are partitioned into the image processor into a first portion of the image frames for the detection of the traffic sign and into a second portion of the image frames for the other driver assistance function. Upon detecting an image suspected to be of the traffic sign in at least one of said image frames of the first portion, the image is tracked in at least one of the image frames of the second portion.

Systems and methods for vehicle navigation

A sparse map 3940 generator system creates maps used in navigating autonomous or partially autonomous vehicles. Drive information is received from each of a plurality of vehicles distributed across a plurality of vehicle groups that traverse a road junction 3940, the road junction including several entrances and exits. Each vehicle group 3900 includes one or more vehicles that traverse a different entrance-exit combination . The drive information from each of the plurality of vehicles includes three-dimensional feature points associated with objects detected by analyzing images captured as a particular vehicle traversed a particular entrance-exit combination. The three-dimensional feature points are aligned to generate several aligned three dimensional feature point groups 3900, one for each entrance-exit combination of the road junction. A sparse map 3940 is generated based on a correlation of the one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included every other aligned three-dimensional feature point group 3900, the sparse map including at least one target trajectory associated with each of the entrance-exit combinations of the road junction.

Sparse map for autonomous vehicle navigation

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters. The sparse map has a data density of no more than 1 megabyte per kilometer.

System and methods for vehicle navigation

Systems and methods are provided for vehicle navigation. In one implementation, a system for correlating 3830 information collected from a plurality of vehicles relative to a common road segment comprises a processor programmed to receive 3820, from first and second vehicles, drive information including indicators of positions associated with detected semantic and non-semantic road features; correlate the drive information, wherein the correlating includes determining a refined position of the detected semantic and non-semantic road features based on the indicators of positions associated with the detected semantic and non-semantic road features; store 3840 the refined positions of the detected semantic and non-semantic road features in a map; and distribute 3850 the map to one or more vehicles for use in navigating the one or more vehicles along the common road segment.

Detektion und erkennung von verkehrszeichen

Systems and methods for vehicle navigation

A navigation system for a vehicle which receives 4010, from a camera of the vehicle, a plurality of images captured from an environment of the vehicle. A first image is analysed 4020 to identify a non-semantic road feature, and a second image is analysed 4040 to identify a representation of the non-semantic road feature. Three dimensional coordinates for a point associated with the non-semantic road feature are determined 4060 based on a difference between the first and second image locations and on motion information for the vehicle between a capture of the first image and a capture of the second image. The three-dimensional coordinates for the point associated with the non-semantic road feature are sent 4070 to a server for use in updating a road navigation model. The non-semantic road feature may be the back of a sign, a building, a light pole, a road crack, or a pothole.

Systems and methods for navigating lane merges and lane splits

Systems and methods for vehicle navigation

Systems and methods for vehicle navigation

Systems and methods for vehicle navigation

A navigational map for one or more road segments is created by collecting 5101 navigational information associated with an environment traversed by a host vehicle. The vehicle location is determined 5103, based on outputs associated with one or more sensors and it is also determined 5103 whether the location of the host vehicle is at or within a predetermined distance from a geographical region of interest. Based on the determination that the location of the host vehicle is at or within the predetermined distance from the geographical region of interest, second navigational information associated with the environment is collected 5104, wherein the second navigational information includes a plurality of three-dimensional feature points and is associated with a second aspect that is different than the first aspect. At least one of the collected first navigational information or the collected second navigational information are uploaded 5105 from the host vehicle and the navigational map is updated 5106 based on the uploaded at least one of the collected first navigational information or the collected second navigational information.

Systems and methods for vehicle navigation

Systems and methods for navigating lane merges and lane splits

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Systems and methods for navigating lane merges and lane splits

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Low resolution traffic light candidate identification followed by high resolution candidate analysis

Systems and methods are provided for vehicle navigation. The systems and methods may detect traffic lights. For example, one or more traffic lights may be detected using detection-redundant camera detection paths, a fusion of information from a traffic light transmitter and one or more cameras, based on contrast enhancement for night images, and based on low resolution traffic light candidate identification followed by high resolution candidate analysis. Additionally, the systems and methods may navigation based on a worst time to red estimation.