A vehicle control device includes a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle, a deriver configured to derive a predicted probability that the other vehicle will travel in the future along each of routes which are assumed when a plurality of routes along which the other vehicle is able to travel are assumed on a road on which the other vehicle recognized by the recognizer travels, and a travel controller configured to control behavior of the vehicle based on the predicted probability derived by the deriver.

Systems and methods for driver dazzle detection for a subject vehicle, may include: using a plurality of sensors to gather data regarding driver characteristics and light source characteristics in an environment of the subject vehicle; evaluating sensor data from the plurality of sensors received to determine at least one of a driver characteristic and a light source characteristic; determining a level of dazzling of a driver of the vehicle based on the determined at least one of a driver characteristic and a light source characteristic; and engaging remedial action based on the determined level of dazzle of the driver of the vehicle, wherein the remedial action comprises at least one of switching a control of the vehicle from a manual drive mode to an autonomous drive mode and engaging an ADAS feature if it is detected that the determine level of dazzling is above a dazzling threshold.

A method for the performance-enhancing driver assistance of a road vehicle driven on a track. The method comprises the steps of defining a dynamic model of the road vehicle, determining the actual position and orientation of the road vehicle, detecting a plurality of space data concerning the structure of the track, detecting a plurality of dynamic data of the vehicle, determining a passing through point of the road vehicle arranged in front of the road vehicle, solving an optimum control problem aimed at optimizing a cost function, taking into account, as boundary conditions, the plurality of environmental data, the actual position and the passing through point, so as to compute a mission optimizing the cost function, and driving the vehicle in an autonomous manner so as to show to the driver the mission optimizing the cost function.

A method for the performance-enhancing driver assistance of a road vehicle driven by a driver. The method comprises the steps of detecting a plurality of dynamic data (DD) of the vehicle by means of a control system, suggesting to the driver, by means of an interface device and depending on the plurality of dynamic data, one or more corrective actions to be carried out in order to accomplish a mission optimizing a cost function, and estimating the driving ability of the driver in order to obtain a driver rating value, based on which the corrective actions to be suggested are to be changed.

A method for the performance-enhancing driver assistance of a road vehicle. The method comprises the steps of defining a dynamic model of the road vehicle, determining the actual position and orientation of the road vehicle, detecting a plurality of environmental data, detecting a plurality of dynamic data, determining a passing through point of the road vehicle arranged in front of the road vehicle, solving an optimum control problem aimed at optimizing a cost function), taking into account, as boundary conditions, the plurality of environmental data, the actual position and the passing through point, so as to compute a mission optimizing the cost function from the actual position of the vehicle to the passing through point of the vehicle.

Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a reference trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment. A point density can be determined for various portions of the reference trajectory. In some cases, the point density can be based at least in part on a cost associated with a curvature value associated the reference trajectory or a cost associated with a distance between the reference trajectory and an obstacle in the environment. Further, the techniques can include evaluating a cost function at points on the reference trajectory to generate a target trajectory with respect to the reference trajectory, and controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory.

The invention relates to a method for operating a self-propelled motor vehicle having a plurality of control units and a plurality of program codes for controlling the function of autonomous driving and possibly other functions of the self-propelled vehicle, wherein a plurality of program codes used for an autonomous driving mode are redundantly applied to at least two different control units. In doing so, the self-propelled motor vehicle is operated in an at least partially autonomous driving mode. In this mode, the functions directly needed to satisfy the passenger's wishes are ascertained and weighted corresponding to their relevance for satisfying the passenger's wishes. In so doing, the functions, or the scope of functions, are released depending on the achievement of a target achievement level.

Systems and methods for generating motion plans for autonomous vehicles are provided. An autonomous vehicle can include a machine-learned motion planning system including one or more machine-learned models configured to generate target trajectories for the autonomous vehicle. The model(s) include a behavioral planning stage configured to receive situational data based at least in part on the one or more outputs of the set of sensors and to generate behavioral planning data based at least in part on the situational data and a unified cost function. The model(s) includes a trajectory planning stage configured to receive the behavioral planning data from the behavioral planning stage and to generate target trajectory data for the autonomous vehicle based at least in part on the behavioral planning data and the unified cost function.

An apparatus for controlling backward driving of a vehicle including: a driving trajectory generation unit configured to generate a driving trajectory for backward driving of an ego vehicle on a target path, using sensing information acquired while the ego vehicle drives forward along the target path; and a control unit configured to control the backward driving of the ego vehicle on the target path according to the driving trajectory generated by the driving trajectory generation unit, correct the driving trajectory using driving information of another vehicle, which has driven backward on the target path before the ego vehicle, when a change on the target path is sensed in comparison to during the forward driving of the ego vehicle during the process of controlling the backward driving of the ego vehicle, and control the backward driving of the ego vehicle according to the corrected driving trajectory.

A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.