System, methods, and other embodiments described herein relate to improving right-of-way determinations at an intersection. In one embodiment, a method includes acquire, in a lead vehicle that is a cloud leader of a micro-cloud, observations about the intersection for a set of vehicles including at least one remote vehicle. The set of vehicles are approaching the intersection. The remote vehicle and the lead vehicle are members of the micro-cloud. The method includes deriving an assignment of right-of-ways indicating an order about how the set of vehicles may proceed through the intersection. The method includes providing the assignment to at least the remote vehicle to control right-of-way at the intersection.

A system and method for providing an RNN-based human trust model that include receiving a plurality of inputs related to an autonomous operation of a vehicle and a driving scene of the vehicle and analyzing the plurality of inputs to determine automation variables and scene variables. The system and method also include outputting a short-term trust recurrent neural network state that captures an effect of the driver's experience with respect to an instantaneous vehicle maneuver and a long-term trust recurrent neural network state that captures the effect of the driver's experience with respect to the autonomous operation of the vehicle during a traffic scenario. The system and method further include predicting a take-over intent of the driver to take over control of the vehicle from an automated operation of the vehicle during the traffic scenario.

A method for planning a driving trajectory defining a travelling path for a vehicle, wherein the driving trajectory is intended to be followed by executing one or more vehicle manoeuvres and wherein the vehicle comprises at least one environment perception sensor having a sensor field of view he method includes planning the driving trajectory from a first point to a second point, by including at least one additional vehicle manoeuvre resulting in a deviation from an otherwise expected travelling path from the first point to the second point, so that each section of an area which the vehicle is intended to pass from the first point to the second point is covered in the sensor field of view at least one time during driving from the first point to the second point and at least before passing each respective section.

Provided are methods for predicting motion of hypothetical agents, which can include receiving sensor data, generating a segmentation mask indicative of at least one occluded area, generating at least one hypothetical agent trajectory, determining at least one agent generation point, determining whether a threshold distance from the at least one agent generation point to the vehicle is satisfied, generating at least one agent, planning a path of the vehicle and controlling the vehicle according to the planned path. Systems and computer program products are also provided.

A method for controlling an automated vehicle. In a first method part, the instantaneous surroundings of the automated vehicle are detected using an on-board surroundings sensor system. A localization of the automated vehicle takes place based on a comparison of the data of the surroundings sensor system to a previously provided HD localization map. A map-based surroundings model is generated, in a second method part representing a normal mode, and is used for planning the behavior and the trajectory of the automated vehicle. In a third method part representing a safety mode, carried out in parallel to or as an alternative to the first method part, the instantaneous surroundings of the automated vehicle are detected using the on-board surroundings sensor system. Based on the data ascertained in the process, a map-less surroundings model is generated and used for planning the behavior and the trajectory of the automated vehicle.

A rearwardly directed movement of a first vehicle that has a driving assistance system that can be controlled, the driving assistance system having at least one sensor and a control device, in which threshold values for triggering a warning intervention and/or braking intervention in respect of the interval in relation to at least one further road user are set, and additionally being connected to or having a camera, by which activated travel-direction indicators of a further road user are recognized, which travel-direction indicators are included in the calculation of a probable trajectory of the further road user, and a probability of a collision is calculated, according to which the threshold values are lowered or raised.

A method for the geometric representation of a vehicle area (1) of a vehicle for the purpose of collision detection, wherein the vehicle area (1) has a boundary (2), comprising the method steps of performing a medial axis transformation of the vehicle area (1) to generate a vehicle area skeleton (4) and performing a point classification of points of the vehicle area skeleton (4) to determine front corner region points (5, 6) and rear corner region points (7, 8), and a front wheelbase point (9) and a rear wheelbase point (10), and also performing a circle decomposition of the vehicle area (1), wherein each circle of the circle decomposition has a maximum area exceedance value (17).

A traffic safety control method is provided. The method includes obtaining first related information of a road when a vehicle is traveling on a road. Second related information is detected using a detecting device of the vehicle when the first related information indicates that there is the intersection in front of the vehicle on the road. The vehicle is controlled according to the first related information and the second related information.

An apparatus and method for controlling lane following include acquiring front image information through a camera installed at an ego-vehicle during travel of the ego-vehicle; setting a target trajectory of the ego-vehicle using line information and travel information of a preceding vehicle extracted based on the image information; and calculating a steering torque to control steering of the ego-vehicle along the set target trajectory.

A hazard prediction and a hazard avoidance procedure are performed based on a predetermined hazard avoidance condition during an autonomous driving operation of a vehicle. User-input information about a driving operation of the vehicle; is received, and area or object information is checked to determine the area or the object as a monitoring target. The monitoring target is tracked. At least one additional hazard avoidance procedure for the monitoring target is set, and the at least one additional hazard avoidance procedure is performed in addition to the hazard avoidance procedure when the monitoring target approaches the vehicle.