Abstract: An action planning system (100) and method for autonomous vehicles are provided. The system (100) comprises one or more processors (108) and one or more non-transitory computer-readable storage medium (110) having stored thereon a computer program used by the one or more processors (108), wherein the computer program causes the one or more processors (108) to estimate future environment of an autonomous vehicle (114), generate a possible trajectory for the autonomous vehicle (114), predict motion and reactions of each dynamic obstacle in the future environment of the autonomous vehicle (114) based on current local traffic context, and generate a prediction iteratively over timesteps.

Abstract: A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

Abstract: A method of operating a vehicle includes determining a first plurality of known lane intervals at a first time with a controller of a guidance system of the vehicle, the known lane intervals available to receive the vehicle, determining a plurality of visible lane intervals at a second time after the first time with the controller based on perception data generated by the guidance system, and determining a plurality of occupied lane intervals at the second time with the controller based on the perception data generated by the guidance system. The method further includes determining a second plurality of known lane intervals at the second time as a union of the first plurality of known lane intervals, the plurality of visible lane intervals, and the plurality of occupied lane intervals.

Abstract: A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

Abstract: An action planning system (100) and method for autonomous vehicles are provided. The system (100) comprises one or more processors (108) and one or more non-transitory computer-readable storage medium (110) having stored thereon a computer program used by the one or more processors (108), wherein the computer program causes the one or more processors (108) to estimate future environment of an autonomous vehicle (114), generate a possible trajectory for the autonomous vehicle (114), predict motion and reactions of each dynamic obstacle in the future environment of the autonomous vehicle (114) based on current local traffic context, and generate a prediction iteratively over timesteps.

Abstract: A method of operating a vehicle includes determining a first plurality of known lane intervals at a first time with a controller of a guidance system of the vehicle, the known lane intervals available to receive the vehicle, determining a plurality of visible lane intervals at a second time after the first time with the controller based on perception data generated by the guidance system, and determining a plurality of occupied lane intervals at the second time with the controller based on the perception data generated by the guidance system. The method further includes determining a second plurality of known lane intervals at the second time as a union of the first plurality of known lane intervals, the plurality of visible lane intervals, and the plurality of occupied lane intervals.

Abstract: A method of operating an autonomous vehicle on a roadway includes generating stereo vision data with a stereo vision camera of a vehicle guidance system of the autonomous vehicle, the stereo vision data representative of a traffic light on the roadway, generating disparity map data with a controller of the vehicle guidance system based on the stereo vision data, and generating odometry data of the vehicle at a first time and at a second time after the first time with an odometry system of the autonomous vehicle. The method further includes determining a position of the traffic light based on the disparity map data at the first time, determining a predicted position of the traffic light in the disparity map data at the second time based on the odometry data, and determining a state of the traffic light at the predicted position.

Abstract: A processing system includes at least one computer processor, which is configured to implement a method that includes receiving crowd-sourced driving path data, map data, and real-time sensor data associated with a roadway. The method includes generating, by the processing system, optimized driving path data for the roadway based on the crowd-sourced path data, the map data, and the sensor data. The method includes providing control signals to control an autonomous or semi-autonomous vehicle based on the optimized driving path data.

Abstract: A processing system includes at least one computer processor, which is configured to implement a method that includes receiving crowd-sourced driving path data, map data, and real-time sensor data associated with a roadway. The method includes generating, by the processing system, optimized driving path data for the roadway based on the crowd-sourced path data, the map data, and the sensor data. The method includes providing control signals to control an autonomous or semi-autonomous vehicle based on the optimized driving path data.