Technologies and techniques for determining a trajectory of an assisted-operated motor vehicle. At least one object is detected in an environment of the motor vehicle and at least one uncertainty with respect to the object is determined. A future environment with the object is predicted via an electronic computing device, as a function of the detected environment and the detected object, wherein a risk value for a planned trajectory is determined on the basis of a collision probability. A most probable impact constellation and accident severity for the most probable impact constellation is determined, wherein the collision probability and the accident severity is weighted in a risk value, and wherein the trajectory is determined as a function of the determined risk value.

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.

The techniques and systems herein enable vehicle positioning for V2V optimization. Specifically, responsive to determining that a quality of a V2V connection between the host vehicle and a target does not satisfy a threshold, a following position of the host vehicle relative to the target and whether the following position is within a predicted null position of the V2V connection and closer to the target than a selected position are determined. The following position is then controlled based on whether the following position is within the predicted null position and whether the following position is closer to the target than the selected position. By controlling the following position based on the quality of the V2V connection and the null position, more-reliable V2V connections may be maintained, thus enabling better performance of downstream operations.

A vehicle control method according to an embodiment includes selecting a target vehicle to share low-level sensor information with based on the low-level sensor information obtained by a host vehicle and information on a structure included in high-definition map information, and receiving low-level sensor information of the structure from the target vehicle to update a sensor data map of the host vehicle, and performing map matching with the high-definition map information through feature points of a road extracted based on the updated sensor data map.

There is provided mechanisms for AD, or at least ADAS, aided manoeuvring of a vehicle. A method includes selecting between using a CRLS based positioning system and a GNSS based positioning system for aiding manoeuvring of the vehicle. Which positioning system to select is based on comparing a first error determined for the CRLS based positioning system to a second error determined for the GNSS based positioning system. The positioning system with smallest error is selected. The method comprises aiding the manoeuvring of the vehicle using the selected positioning system. The manoeuvring pertains to reversing of the vehicle.

In one embodiment, a computing system of a vehicle may receive perception data associated with a scenario encountered by a vehicle while operating in an autonomous driving mode. The system may identify the scenario based at least on the perception data. The system may generate a performance metric associated with a vehicle navigation plan to navigate the vehicle in accordance with the identified scenario. In response to a determination that the performance metric associated with the vehicle navigation plan fails to satisfy one or more criteria for navigating the vehicle in accordance with the identified scenario, the system may trigger a disengagement operation related to disengaging the vehicle from the autonomous driving mode. The system may generate a disengagement record associated with the triggered disengagement operation. The disengagement record may include information associated with the identified scenario encountered by the vehicle related to the disengagement operation.

A vehicle control device, a vehicle control method, a vehicle motion control system, and a lane estimation device according to the present invention obtain first information on lane markings defining a lane in which a vehicle travels based on external information obtained from an external recognition unit, obtain second information on a curvature of the lane based on information on a road shape obtained from a road shape information acquisition unit, obtain third information on a behavior of the vehicle based on a physical quantity that is related to a motion state of the vehicle and obtained from a vehicle motion state detection unit, and estimate lane information including information on curvatures of the lane markings and information on relative positions of the vehicle with respect to the lane markings based on the first information, the second information, and the third information.

Disclosed embodiments provide a technical improvement for providing localization for a transportation vehicle by detecting road wear reference lines in a roadway on which the transportation vehicle is travelling and controlling, guiding or otherwise facilitating alignment of the transportation vehicle wheel centers with the detected centers of the road wear.

A system for controlling an autonomous vehicle when the autonomous vehicle is outside of its operational design domain. The system includes an environment detection system, a vehicle control system, and a first electronic processor. The first electronic processor is configured to detect that an autonomous vehicle is outside of its operational design domain and send a first electronic message. The first electronic message requests that a surrounding vehicle lead the autonomous vehicle until the autonomous vehicle returns to its operational design domain or reaches a predetermined location. The electronic processor is also configured to determine a leading vehicle and control the autonomous vehicle to follow the leading vehicle until the autonomous vehicle returns to its operational design domain or reaches the predetermined location.

A method determines a quality characteristic, in which the quality characteristic is representative for the procurement of a vehicle-specific friction coefficient of a vehicle and a standardized friction coefficient of a friction coefficient map. The friction coefficient data are received, the friction coefficient data being representative for a friction coefficient measured depending upon the position of a vehicle and a quality characteristic representative for the vehicle. The friction coefficient map is provided, the friction coefficient being representative for standardized friction coefficients of a vehicle fleet of a route network. Depending upon the friction coefficient data and the friction coefficient map, the quality characteristic is determined again and transmitted to the vehicle.