Abstract: A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

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 for a vehicle, which drives in an at least in-part-automated manner is configured to determine a control signal for a control system. The system includes a sensor, a planning module, and a monitoring module. The sensor is configured to detect an object in a surrounding area of the vehicle and store a corresponding object representation. The planning module is configured to determine, based to the stored object representation, a first trajectory and a first probability of collision of the first trajectory for the vehicle. The monitoring module is configured to perform one of following actions when the first probability of collision exceeds a predefined probability of collision: determine, using the planning module and based on the stored object representation, a further trajectory having a further probability of collision and a maximum deceleration of the further trajectory; or assess the stored object representation of the object using the sensor.

Abstract: Systems and methods for vehicle motion planning. The system includes an electronic controller configured to access a manifold describing a driving surface in three dimensions from a memory, perform parameterization of the manifold to obtain a plurality of local charts describing the driving surface in two dimensions, determine a driving path of a vehicle based using the plurality of local charts, and generate a command to navigate the vehicle along the driving path.

Abstract: A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

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 for a vehicle, which drives in an at least in-part-automated manner is configured to determine a control signal for a control system. The system includes a sensor, a planning module, and a monitoring module. The sensor is configured to detect an object in a surrounding area of the vehicle and store a corresponding object representation. The planning module is configured to determine, based to the stored object representation, a first trajectory and a first probability of collision of the first trajectory for the vehicle. The monitoring module is configured to perform one of following actions when the first probability of collision exceeds a predefined probability of collision: determine, using the planning module and based on the stored object representation, a further trajectory having a further probability of collision and a maximum deceleration of the further trajectory; or assess the stored object representation of the object using the sensor.

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.

Abstract: A yield determination system for automatically collecting, determining, and labeling yield behaviors of vehicles during cooperative driving scenarios. The system includes sensors for detecting the start and stop of the scenario, a data recorder for automatically collecting the data, an annotation unit for automatically labeling features of interest about the vehicle and surrounding vehicles during the scenario. The labeled file may be automatically uploaded and processed to insert the labelled features into a learning model to predict vehicle behavior in cooperative driving scenarios.