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 ...

Free Space Mapping and Navigation

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. 177.-. (canceled)78. A system for mapping road segment free spaces for use in autonomous vehicle navigation , the system comprising: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment;', 'update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and', 'distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles., 'at least one processor programmed to79. The system of claim 78 , wherein the lateral region of free space includes one or more of a driveway intersecting the road segment claim 78 , a parking lot claim 78 , or a sidewalk.80. The system of claim 78 , wherein the lateral region of free space includes one or more of a driveway intersecting the road segment claim 78 , a parking lot claim 78 , or a sidewalk.81. The system of claim 78 , wherein the at least one processor is further programmed to receive an additional characteristic associated with the lateral free space adjacent to the road segment.82. The system of claim 81 , wherein the additional characteristic includes a free space type indicator.83. The system of claim 82 , wherein the free space type indicator includes at least one of a driveway claim 82 , a parking ...

NAVIGATION BASED ON TRAFFIC LIGHT CYCLE PREDICTION

A system for mapping traffic lights and associated traffic light cycle times for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive at least one location identifier associated with a traffic light detected along a road segment; receive at least one indicator of traffic light state timing associated with the detected traffic light; and update an autonomous vehicle road navigation model relative to the road segment. The update can be based on the at least one location identifier and based on the at least one indicator of traffic light state timing associated with the traffic light detected along the road segment. The processor may also include distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. 1127.-. (canceled)128. A system for mapping traffic lights and associated traffic light cycle times for use in autonomous vehicle navigation , the system comprising at least one processor programmed to:receive, from a first vehicle, at least one location identifier associated with a traffic light detected along a road segment;receive, from the first vehicle, at least one indicator of traffic light state timing associated with the detected traffic light;update an autonomous vehicle road navigation model relative to the road segment, wherein the update is based on the at least one location identifier and based on the at least one indicator of traffic light state timing associated with the traffic light detected along the road segment; anddistribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.129. The system of claim 128 , wherein the autonomous vehicle road navigation model includes a mapped location of the detected traffic light claim 128 , and wherein the at least one location identifier received from the first vehicle is used to update the mapped location of the detected traffic light.130. The system of claim 129 , wherein the ...

LANE MAPPING AND NAVIGATION

Systems and methods are disclosed for mapping lanes for use in vehicle navigation. In one implementation, at least one processing device may be programmed to receive navigational information from a first vehicle and a second vehicle that have navigated along a road segment including a lane split feature; receive at least one image associated with the road segment; determine, from the first navigational information, a first actual trajectory of the first vehicle and a second actual trajectory of the second vehicle; determine a divergence between the first actual trajectory and the second actual trajectory; determine, based on analysis of the at least one image, that the divergence between the first actual trajectory and the second actual trajectory is indicative of the lane split feature; and update a vehicle road navigation model to include a first target trajectory and a second target trajectory that branches from the first target trajectory after the lane split feature. 1. A system for mapping lane splits for use in vehicle navigation , the system comprising:{'claim-text': ['receive first navigational information from a first vehicle that has navigated along a road segment, wherein the road segment includes a lane split feature, and wherein the road segment includes at least a first travel lane prior to the lane split feature that transitions into at least a second travel lane and a third travel lane after the lane split feature;', 'receive second navigation information from a second vehicle that has navigated along the road segment;', 'receive at least one image associated with the road segment;', 'determine, from the first navigational information, a first actual trajectory of the first vehicle along the first travel lane and the second travel lane of the road segment;', 'determine, from the second navigational information, a second actual trajectory of the second vehicle along the first travel lane and the third travel lane of the road segment;', 'determine a ...

Relevant Traffic Light Mapping and Navigation

A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. 1107.-. (canceled)108. A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation , the system comprising at least one processor programmed to:receive, from a first vehicle, at least one location identifier associated with a traffic light detected along a road segment;receive, from the first vehicle, a state identifier associated with the traffic light detected along the road segment;receive, from the first vehicle, navigational information indicative of one or more aspects of motion of the first vehicle along the road segment;determine, based on the navigational information associated with the first vehicle, a lane of travel traversed by the first vehicle along the road segment;determine, based on the navigational information and based on the state identifier received from the first vehicle, whether the traffic light is relevant to the lane of travel traversed by the first vehicle;update an autonomous vehicle road navigation model relative to the road segment, wherein the update is based on the at least one location identifier and based on ...

SYSTEMS AND METHODS FOR ESTIMATING FUTURE PATHS

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location. 1. A system for estimating a future path ahead of a current location of a vehicle , comprising:at least one processor programmed to:obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road;obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads;apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; andprovide, based on application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.2. The system according to claim 1 , wherein the trained system comprises piece-wise affine functions of global functions.3. The system according to claim 2 , wherein the global functions comprise convolutions claim 2 , max pooling claim 2 , or a rectifier liner unit.4. The system according to claim 2 , wherein the at least one processor is further programmed to: utilize the estimated future path ahead of the current location of the vehicle to control at least one electronic or mechanical unit of the vehicle to change at least one motion parameter of the vehicle.5. The system according to claim 2 , wherein the at least one processor is further programmed to: utilize the estimated ...

SYSTEMS AND METHODS FOR DETECTING TRAFFIC SIGNAL DETAILS

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a traffic light detection system is disclosed. The system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a traffic lamp fixture having at least one traffic light. At least one processing device may be configured to align areas of the plurality of images corresponding to the traffic light, based on a determined center of brightness, expand each pixel within the aligned areas, determine a set of average pixel values including an average pixel value for each set of corresponding expanded pixels within the aligned areas, and determine, based on the set of average pixel values, whether the traffic light includes an arrow. 1. A traffic light detection system for a vehicle , the system comprising:at least one image capture device configured to acquire a plurality of images 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 plurality of images via the data interface;', 'align areas of the plurality of images corresponding to the traffic light, based on a determined center of brightness;', 'expand each pixel within the aligned areas;', 'determine a set of average pixel values including an average pixel value for each set of corresponding expanded pixels within the aligned areas; and', 'determine, based on the set of average pixel values, whether the traffic light includes an arrow., 'at least one processing device configured to2. The traffic light detection system of claim 1 , wherein expansion of each pixel within the aligned areas includes expanding each pixel into a 3×3 matrix of pixels.3. The traffic light detection system of claim 1 , wherein expansion of each pixel within the aligned areas includes expanding each pixel into a 4×4 matrix of pixels.4. The traffic light detection system ...

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.

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 lane end recognition

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.

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 estimating future paths

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

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.

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.

Systems and methods for autonomous vehicle navigation

Relevant traffic light mapping and navigation

A system for mapping traffic lights and for determining traffic light relevancy for use in autonomous vehicle navigation. The system may include at least one processor programmed to: receive at least one location identifier associated with a traffic light; receive a state identifier associated with the traffic light; receive navigational information indicative of one or more aspects of motion of the first vehicle along the road segment, and determine, based on the navigational information, a lane of travel traversed by the first vehicle along the road segment. The processor may also determine whether the traffic light is relevant to the lane of travel traversed by the first vehicle; update an autonomous vehicle road navigation model relative to the road segment; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

Systems and methods for autonomous vehicle navigation

Lane mapping and navigation

Systems and methods for autonomous vehicle navigation

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Systems and methods for autonomous vehicle navigation

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Systems and methods for autonomous vehicle navigation

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

Lane mapping and navigation

Systems and methods are disclosed for mapping lanes for use in vehicle navigation. In one implementation, at least one processing device may be programmed to receive navigational information from a first vehicle and a second vehicle that have navigated along a road segment including a lane split feature; receive at least one image associated with the road segment; determine, from the first navigational information, a first actual trajectory of the first vehicle and a second actual trajectory of the second vehicle; determine a divergence between the first actual trajectory and the second actual trajectory; determine, based on analysis of the at least one image, that the divergence between the first actual trajectory and the second actual trajectory is indicative of the lane split feature; and update a vehicle road navigation model to include a first target trajectory and a second target trajectory that branches from the first target trajectory after the lane split feature.

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.