A method for controlling a parked vehicle includes receiving vehicle identification information from a first terminal, specifying a target vehicle to be controlled based on the vehicle identification information, waking up the target vehicle to be controlled and transitioning the target vehicle to be controlled to a movable state, and transmitting a moving direction to the target vehicle to be controlled.

Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.

A computer-implemented method and system for generating a virtual environment for a vehicle for testing highly automated driving functions of a motor vehicle. The method comprises projecting the pixel-based classified camera image data onto the pre-acquired LiDAR point cloud data, wherein each point of the LiDAR point cloud, superimposed by classified pixels of the camera image data, in particular having the same image coordinates, is assigned an identical class and an instance segmentation of the classified LiDAR point cloud data for determining at least one real object comprised by a class. A computer program and a computer-readable data carrier are also provided.

This application is directed to on-vehicle behavior modeling of vehicles. A vehicle has one or more processors, memory, a plurality of sensors, and a vehicle control system. The vehicle collects training data via the plurality of sensors, and the training data include data for one or more vehicles during a collection period. The vehicle locally applies machine learning to train a vehicle driving behavior model using the collected training data. The vehicle driving behavior model is configured to predict a behavior of one or more vehicles. The vehicle subsequently collecting sensor data from the plurality of sensors and drives the vehicle by applying the vehicle driving behavior model to predict vehicle behavior based on the collected sensor data. The vehicle driving behavior model is configured to predict behavior of an ego vehicle and/or a distinct vehicle that appears near the ego vehicle.

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.

Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.

The disclosure provides a method for generating a simulation scene. The method includes: obtaining traffic element information related to intersections contained in a map to be processed, first driving parameter information of a target vehicle, and second driving parameter information of an obstacle vehicle; generating a first trajectory corresponding to the target vehicle based on the first driving parameter information and the traffic element information; generating a second trajectory corresponding to the obstacle vehicle based on the second driving parameter information and the traffic element information; and generating a simulation scene based on the first trajectory, the second trajectory, the first driving parameter information, the second driving parameter information, and the traffic element information.

Provided is an automated valet parking system that automatically moves an autonomous driving vehicle to a pick-up space of a parking place by issuing an instruction to the autonomous driving vehicle parked in the parking place according to a pick-up request from a user. The system includes a user position determination unit configured to determine whether or not a user frontend of the user is located in a preset pick-up area or a preset near-pick-up area including the pick-up space, a pick-up request reception unit configured to receive the pick-up request when the user position determination unit determines that the user frontend is located in the pick-up area or the near-pick-up area, and a vehicle instruction unit configured to instruct the autonomous driving vehicle that is a target of the pick-up request to move to the pick-up space when the pick-up request reception unit receives the pick-up request.

Techniques for presenting a user interface on a display for monitoring and/or controlling a vehicle. The user interface may include a digital representation of an environment in which the vehicle is operation. Additionally, the user interface may include system interface that is configured to display one or more notifications associated with systems or components of the vehicle. The system interface may additionally, or alternatively, comprise one or more control inputs for controlling systems or components of the vehicle. The user interface may also include a mission interface that includes a map interface and a mission selection interface for selecting routes the vehicle is to navigate. The user interface may additionally cause presentation of visual indicators that indicate the presence of an object that is not shown on the user interface, but that is moving in a direction such that the object will soon be visibly displayed on the user interface.

The technology relates to controlling a vehicle in an autonomous driving mode in accordance with behavior predictions for other road users in the vehicle's vicinity. In particular, the vehicle's onboard computing system may predict whether another road user will perform a continuing lane driving operation, such as going straight in a turn-only lane. Sensor data from detected/observed objects in the vehicle's nearby environment may be evaluated in view of one or more possible behaviors for different types of objects. In addition, roadway features, in particular whether lane segments are connected in a roadgraph, are also evaluated to determine probabilities of whether other road users may make an improper continuing lane driving operation. This is used to generate more accurate behavior predictions, which the vehicle can use to take alternative (e.g., corrective) driving actions.