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.

A travel control device includes a travel environment recognizer recognizing travel environment information around a vehicle, an obstacle recognizer recognizing an obstacle possibly colliding with the vehicle, and a travel controller performing emergency braking when a first parameter becomes a first threshold value or smaller and causing the vehicle to start moving again when a second parameter reaches a second threshold value or larger. The obstacle recognizer recognizes a group of moving objects expressing similar behaviors based on the travel environment information, and recognizes the moving-object group as an obstacle if located closer to the vehicle than other objects. The travel controller calculates an expectation value based on each moving-object's behavior when the vehicle makes an emergency stop, and allows the vehicle to start moving again when the expectation value reaches a threshold value or larger even if the second parameter is smaller than the second threshold value.

A vehicle control device configured to recognize a surrounding situation of a host vehicle, control at least a speed of the host vehicle based on the surrounding situation, determine a target relative position between two other vehicles in a state in which there is no vehicle in a second lane when the host vehicle is allowed to perform merge-point-related movement or a lane change from a first lane to the second lane, determine a target speed serving as a target when the host vehicle reaches the target relative position, and adjust the speed of the host vehicle according to pole-assignment control based on a position deviation that is a deviation between the target relative position and a position of the host vehicle in a road extension direction and a vehicle speed deviation that is a deviation between the speed of the host vehicle and the target speed.

This document discloses system, method, and computer program product embodiments for image-based pedestrian speed estimation. For example, the method includes receiving an image of a scene, wherein the image includes a pedestrian and predicting a speed of the pedestrian by applying a machine-learning model to at least a portion of the image that includes the pedestrian. The machine-learning model is trained using a data set including training images of pedestrians, the training images associated with corresponding known pedestrian speeds. The method further includes providing the predicted speed of the pedestrian to a motion-planning system that is configured to control a trajectory of an autonomous vehicle in the scene.

The disclosed techniques include: obtaining traveling state information of the autonomous vehicle; obtaining, in response to confirming that there is a traffic object within a preset range for the autonomous vehicle, true motion state information of the traffic object; determining a first control decision for indicating whether the autonomous vehicle is to avoid the traffic object based on the traveling state information of the autonomous vehicle and the true motion state information of the traffic object; predicting first predictive motion state information of the traffic object within a first preset duration based on the true motion state information of the traffic object and the first control decision; and determining a second control decision for the autonomous vehicle based on the traveling state information of the autonomous vehicle and the first predictive motion state information of the traffic object.

A vehicle control device includes: a peripheral situation recognition unit (132) configured to recognize a peripheral situation of an automatic driving vehicle; a driving control unit (140, 160, (138)) configured to control one or both of steering and an acceleration or deceleration speed of the vehicle based on the peripheral situation recognized by the peripheral situation recognition unit; and a potential risk estimation unit (138) configured to estimate presence or absence and classification of a potential risk meeting with an obstacle based on classification of a target recognized by the peripheral situation recognition unit and a positional relation between the vehicle and the target. The driving control unit performs the driving control based on an estimation result of the potential risk estimation unit.

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes, by an object detection system of a vehicle in an environment, detecting an actor that may move within a scene in the environment. The method further includes using context of the scene to determine a reference polyline for the actor and determining a kinematic history of the actor. The method additionally includes using the kinematic history to predict a waypoint, which is a predicted position of the actor at a conclusion of a waypoint time period, and identifying a segment of the reference polyline, the segment extending from a current location to a point along the reference polyline that is closest to the waypoint and determining a trajectory for the actor conditioned by the segment of the reference polyline.

A vehicle control method, a vehicle control apparatus, an electronic device and a self-driving vehicle all relates to the field of self-driving and intelligent transportation technologies. The method includes: when a vehicle is moving, in a case that an occluding object is detected, determining a hard brake speed limit point and a potential collision point according to a planned path of the vehicle and position information of the occluding object; calculating a speed limit value of the hard brake speed limit point based on a distance between the hard brake speed limit point and the potential collision point; in a case that a planned speed of the vehicle at the hard brake speed limit point is less than or equal to the speed limit value, controlling the vehicle to move at the planned speed.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining, from an autonomy system, data indicative of a planned trajectory of the autonomous vehicle through a surrounding environment. The method includes determining a region of interest in the surrounding environment based at least in part on the planned trajectory. The method includes controlling one or more first sensors to obtain data indicative of the region of interest. The method includes identifying one or more objects in the region of interest, based at least in part on the data obtained by the one or more first sensors. The method includes controlling the autonomous vehicle based at least in part on the one or more objects identified in the region of interest.

Methods and systems for collision avoidance between vulnerable road users (VRUs) and vehicles are provided. In one aspect, a method and a system for collision avoidance between vulnerable road users (VRUs) and vehicles based on emitted signal relates to VRUs and vehicles configured to emit and receive a proximity signal pertaining to road usage safety before accidents happen. The method and the system for pedestrian-to-vehicle (P2V) collision avoidance is based on emitted signal at the edge. The usefulness of the method and the system is for providing danger notifications pertaining to the field of road safety, and pertaining to collision avoidance, before accidents happen. The method and the system further relate to precautious collision avoidance notifications using past, current and predicted trajectories of VRUs and vehicles, based on emitted signal at the edge.