Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling Doppler-assisted segmentation of points in a point cloud for efficient object identification and tracking in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, the one or more velocity values and the one or more coordinates obtained for the same instance of time, identifying that the set of the return points is associated with an object in an environment, and causing a driving path of the AV to be determined in view of the object.

Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

A driving assistance system includes a processor and a memory that stores surroundings information indicating the surroundings of a vehicle detected by sensors mounted on the vehicle. The processor is configured to acquire the position of a target in front of the vehicle and the position of the boundary of a roadway area in front of the vehicle based on the surroundings information. The processor is configured to determine whether the target is in the roadway area based on the position of the target and the position of the boundary. The processor is configured to calculate the distance between the target and the boundary when the target is in the roadway area. The processor is configured to determine whether the target is crossing the roadway area based on the relationship between the distance and a time.

A method for planning a behavior of a vehicle with respect to one or more occluded area(s) along a navigation path of the vehicle, wherein the method comprises an occluded area identification step, during which the occluded area(s) is/are identified, and a phantom object generation step, during which at least one phantom object is generated for at least one of the occluded areas, the occluded area(s) is/are defined based on information from a predefined occlusion scenario catalog during the occluded area identification step.

Provided are methods for offline perception motion inference, which can include obtaining map data indicative of an environment and obtaining data associated with at least one agent. The method can include determining a trajectory for the agent and matching the trajectory of the agent with a lane connector. The method can also include determining a traffic light parameter. Systems and computer program products are also provided.

A system for braking a host vehicle. A memory storing instructions executable by a processor includes instructions to actuate friction brakes in the host vehicle when a brake condition occurs. The brake condition includes that the host vehicle is parked on a roadway, a person is located exterior to the host vehicle, and an approaching vehicle will come within a distance threshold of the host vehicle.

A method for predicting at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of a surrounding environment of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account in the prediction.

A vehicle may include a camera obtaining a surrounding image around the vehicle; and a controller configured to derive spatial recognition data by learning the surrounding image of the vehicle as an input value of the controller, derive object recognition data including wheel area data of surrounding vehicles around the vehicle by learning the surrounding image of the vehicle as an input value of the controller, determine a ground clearance between a bottom surface of a vehicle body of the surrounding vehicles and a ground by use of the spatial recognition data and the wheel area data, and control the vehicle to park the vehicle according to the ground clearance.

An external environment recognition device includes: a plurality of external environment recognition sensors each having an information detection unit that detects information of an object outside a vehicle, the plurality of external environment recognition sensors being arranged such that a detection range of the information detection unit includes an overlapping region where at least part of the detection range of the information detection unit overlaps with at least part of the detection range of another one of the information detection units; and a synchronous processing unit that extracts identical objects in the overlapping region from detection results of the external environment recognition sensors, and performs synchronous processing to synchronize the plurality of external environment recognition sensors if there is a deviation in position between the identical objects in the overlapping region.