A method for lane departure assistance, the method may include obtaining sensed information about an environment of the first vehicle, by one of more vehicle sensors of a first vehicle; wherein the environment of the first vehicle comprises a passing lane and a current lane in which the first vehicle is positioned; determining, by a computer of the first vehicle, whether the first vehicle can successfully and lawfully bypass a second vehicle; wherein the determining is executed before starting the bypass of the second vehicle and is based on the sensed information, one or more driving laws, and one or more first vehicle mechanical parameters; generating a driver perceivable indicator that is indicative of the determining; sensing that the driver initiates a bypass of the second vehicle; evaluating, following the start of the bypass of the second vehicle and during at least a majority of the bypass, whether the first vehicle can successfully and lawfully complete the bypass of the second vehicle; and generating another driver perceivable indicator that is indicative of the evaluating.

A method for combating a stop-and-go wave problem using deep reinforcement learning based autonomous vehicles includes selecting one of a plurality of deep reinforcement learning algorithms for training an autonomous vehicle and a reward function in a roundabout environment in which autonomous vehicles and non-autonomous vehicles are driving, determining a deep neural network architecture according to the selected deep reinforcement learning algorithm, learning a policy which enables the autonomous vehicle to drive at a closest velocity to a constant velocity based on state information including a velocity of the autonomous vehicle and a relative velocity and a relative position between the autonomous vehicle and an observable vehicle by the autonomous vehicle at a preset time interval and reward information, using the selected deep reinforcement learning algorithm, and driving the autonomous vehicle based on the learned policy to determine an action of the autonomous vehicle.

A method for operating a driving assistant for the automated lateral guidance of a motor vehicle. The method includes: executing an automated lane guidance of the motor vehicle in a first traffic lane; ascertaining a hindrance situation of a further motor vehicle due to the motor vehicle; decision for the automated lane opening of the first traffic lane taking the hindrance situation into account; executing an automated lane change into a second traffic lane for opening the lane; executing an automated lane guidance in the second traffic lane after opening the lane. A device for carrying out the method is also described.

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.

A vehicle control device includes a first inputter, a second inputter, a mode controller configured to, when the first inputter is operated by a user, determine a driving mode of a vehicle as a first mode, and when the second inputter is operated by the user in the first mode, switch the driving mode from the first mode to a second mode, and a driving controller configured to control at least one of a steering and speed of the vehicle, and the driving controller is configured to control a steering and speed of the vehicle and prohibit change of a path of the vehicle when the driving mode is the first mode, and control a steering and speed of the vehicle and change of the path of the vehicle is allowed when the driving mode is the second mode.

A method for trajectory planning, an apparatus, a storage medium, and an electronic device are provided. A constraint set of a space including a target device is determined according to a velocity of an unmanned device and velocities of designated obstacles, so that during optimization of a preliminary reference trajectory, a solution can be obtained with the space in the constraint set as a solution space under the constraint of the constraint set, so as to ensure that the solution space is a convex space, and relatively satisfactory reference trajectory points can be solved.

A vehicle includes a sensor circuit configured to detect an obstacle in a first region which is located on the predetermined traveling route and in a second region which is adjacent to the first region on the predetermined traveling route, the second region being farther than the first region. The vehicle enters the first region in a case where: there is no obstacle in the first region; and there is no obstacle in the second region, and does not enter the first region and stops before the first region in a case where: there is no obstacle in the first region; and there is an obstacle in the second region.

A vehicle control device includes an oncoming vehicle detection sensor configured to detect an oncoming vehicle that approaches an own vehicle, and a controller configured to automatically apply brakes to the own vehicle to avoid a collision with the oncoming vehicle detected by the oncoming vehicle detection sensor under a condition that the own vehicle is at least partially in an opposite lane or a planned path of the own vehicle is at least partially in the opposite lane. The controller is configured to set, between the own vehicle and the oncoming vehicle, a virtual area that moves with the oncoming vehicle and that extends in an advancing direction of the oncoming vehicle, and automatically brake the own vehicle to avoid coming into contact with the virtual area to avoid the collision between the own vehicle and the oncoming vehicle.

A vehicle control device of an embodiment includes a recognizer configured to recognize a surrounding environment of a host vehicle and a driving controller configured to perform driving control by controlling one or both of a speed and steering of the host vehicle on the basis of a recognition result of the recognizer. In a case where a speed relationship between the host vehicle and a forward vehicle traveling in front of the host vehicle satisfies predetermined conditions, the driving controller is configured to perform passing control for passing the forward vehicle, and holds feature information of the forward vehicle under predetermined conditions.

A method for determining a risk boundary in response to the plurality of indications of hard braking events wherein the risk boundary is indicative of a plurality of speed flow pairs at which a risk of a hard braking event is below a threshold value, determining, at a road segment level, a set of speed flow pairs of average speed and vehicle count and a plurality of indications of hard braking events , determining a host vehicle speed, and performing at least one of reducing the host vehicle speed and increasing a host vehicle following distance in response to the host vehicle speed exceeding the risk boundary for the vehicle flow density.