Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: 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.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques.

Abstract: 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.

Abstract: A system for identifying a landmark for use in autonomous vehicle navigation is provided. The system includes at least one processor programmed to receive at least one identifier associated with the landmark; associate the landmark with a corresponding road segment; update an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. The at least one identifier is determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle; analysis of the at least one image to identify the landmark in the environment of the host vehicle; and analysis of the at least one image to determine the at least one identifier associated with the landmark.

Abstract: A system for identifying a landmark for use in autonomous vehicle navigation is provided. The system includes at least one processor programmed to receive at least one identifier associated with the landmark; associate the landmark with a corresponding road segment; update an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. The at least one identifier is determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle; analysis of the at least one image to identify the landmark in the environment of the host vehicle; and analysis of the at least one image to determine the at least one identifier associated with the landmark.

Abstract: 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.

Abstract: 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.

Abstract: A method, system and storage device are generally directed to determining for each of a plurality of data points, a neighborhood of data points about each such data point. For each such neighborhood of data points, a projection set of polynomials is generated based on candidate polynomials. The projection set of polynomials evaluated on the neighborhood of data points is subtracted from the plurality of candidate polynomials evaluated on the neighborhood of data points to generate a subtraction matrix of evaluated resulting polynomials. The singular value decomposition of the subtraction matrix is then computed. The resulting polynomials are clustered into multiple clusters and then partitioned based on a threshold.

Abstract: 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.