Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

A method controls a first motor vehicle. The method includes the steps of: i) receiving status data of a roadway section and a geographic position of the roadway section by the first motor vehicle, ii) sensing, as a function of the received status data, the roadway section by a surroundings sensor of the first motor vehicle, and in response thereto, iii) aligning the surroundings sensor with a particular roadway feature of the roadway section, and/or iv) adapting an evaluation of data of the surroundings sensor, and/or v) updating the status data and transmitting the updated status data.

A parking control apparatus includes: a processor configured to generate an outline based on previously parked vehicles, to generate a target reference based on the outline, to select a parking spot for double-parking based on the target reference, and to perform double-parking control on the parking spot when there are no vacant parking spaces by searching for at least a parking space in a parking lot; and a storage configured to store data and algorithms driven by the processor.

A vehicle control device includes a recognizer that is configured to recognize a surrounding situation of a host vehicle and a driving controller that is configured to control acceleration or deceleration and steering of the host vehicle on the basis of a recognition result of the recognizer. The driving controller is configured to cause the host vehicle to operate in at least any of a first driving state and a second driving state in which a rate of automation is higher or tasks required of an occupant are fewer than in the first driving state, and is configured to transition a driving state of the host vehicle to the first driving state on the basis of movement of a rearward vehicle that travels rearward of the host vehicle recognized by the recognizer in a direction of a vehicle width in a case where the host vehicle is operating in the second driving state.

Embodiments is disclosed to detect a locked heading direction of a pedestrian and to predict a path for the pedestrian using the locked heading direction. According to one embodiment, a system perceives an environment of an autonomous driving vehicle (ADV) using one or more image capturing devices. The system detects a pedestrian in the perceived environment. The system determines a facing direction of the pedestrian relative to the ADV as one of left/right side, front, or back. If the facing direction of the pedestrian is determined to be front or back facing, the system determines a lane nearest to the pedestrian. The system projects the pedestrian onto the nearest lane to determine a lane direction at the projection. The system determines a heading direction for the pedestrian locking to the lane direction of the nearest lane based on a predetermined condition.

A method for clearing passage of a tunnel in the automated driving mode of a vehicle involves checking, in a manual driving mode with passive automated driving mode of the vehicle, whether the tunnel is suitable for passage in the automated driving mode of the vehicle. During the check control commands for actuators of a longitudinal and transverse movement of the vehicle are generated, but are not implemented by the actuators. In the event that no unmanageable situation is detected in the tunnel during the check and the generated control commands are plausible in relation to interventions made by a vehicle user in manual driving mode with passive automated driving mode, the tunnel is assessed as suitable for automated driving mode.

A system for navigating a host vehicle may include memory and at least one processor configured to receive a plurality of images acquired by a camera onboard the host vehicle; generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle; determine, based on analysis of at least one of the plurality of images, one or more indicators of an orientation of the host vehicle; and generate, based on the one or more indicators of orientation of the host vehicle and the road geometry model for the segment of road forward of the host vehicle, one or more output signals configured to cause a change in a pointing direction of a movable headlight onboard the host vehicle.

A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.

A vehicle includes a display configured to emit light to a ground surface, a communicator configured to receive a movement signal of the vehicle through communication with an external terminal, and a controller communicatively connected to the communicator and configured to determine a moving area of the vehicle based on the received movement signal of the vehicle, determine a danger area and a safety area included in the moving area, and control the display to emit light to the danger area and the safety area.

A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.