A control system of the autonomous vehicle may generate multiple possible behavior control movements based on the driving goal and the assessment of the vehicle environment. In doing so, the method and system selects one of the best behavior control, among the multiple possible movements, and the selection is based on the quantitative grading of its driving behavior.

Provided are methods for generating high fidelity synthetic sensor data representing hypothetical driving scenarios for the vehicle. Some methods described include accessing sensor data associated with operation of a vehicle in an environment traversing a first path. Operation of a simulated vehicle is simulated along a synthetic driving scenario in the simulated environment along a second path different from the first path and in simulation with the plurality of simulated agents. Systems and computer program products are also provided.

The present disclosure is related to connected vehicles, computer-assisted and/or autonomous driving vehicles, Internet of Vehicles (IoV), Intelligent Transportation Systems (ITS), and Vehicle-to-Everything (V2X) technologies, and in particular, to enhanced collective perception service (CPS) reporting mechanisms. The enhanced collective perception reporting mechanisms utilize multiple collective perception message (CPM) reporting mechanisms to share CPS data with reduced communication overhead, reduced latency, reduced processing complexity, and at the same time, enabling sharing information related to perceived and/or detected objects.

A driver assistance system plans movement for a motor vehicle, wherein a safe state of the motor vehicle is a state of the motor vehicle in a first time step from which the motor vehicle can be transferred, as a function of a movement capability of the motor vehicle in at least one second time step which follows the first time step, into a further safe state without colliding with a road user. The driver assistance system is configured to determine for at least one future time step starting from a current state of the motor vehicle, at least one possible future state of the motor vehicle and of the road user, and to select safe future states of the motor vehicle from the possible future states of the motor vehicle and of the road user, and to plan a movement for the motor vehicle as a function of the safe future states.

System, methods, and other embodiments described herein relate to resolving a standoff by a vehicle. In one embodiment, a method includes generating a happens-before relationship that explains events between the vehicle and other vehicles before the standoff. The standoff may be a dispute for a right of way between the vehicle and the other vehicles. The method also includes identifying the standoff using a causality relationship analysis according to the happens-before relationship. The method also includes generating a mitigation plan for the standoff that forms standoff solutions in association with the standoff being similar to a prior standoff. The method also includes resolving the standoff by causing vehicle maneuvers associated with the vehicle according to the standoff solutions.

Techniques for controlling a vehicle based on height data and/or classification data being determined utilizing multi-channel image data are discussed herein. The vehicle can capture lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.

A vehicle safety system within an autonomous or semi-autonomous vehicle may predict and avoid collisions between the vehicle and other moving objects in the environment. The vehicle safety system may determine one or more perturbed trajectories for another object in the environment, for example, by perturbing the state parameters of a perceived trajectory associated with the object. Each perturbed trajectory may be evaluated to determine whether it intersects or potentially collides the planned trajectory of the vehicle. In some examples, the vehicle safety system may aggregate the results of analyses of multiple perturbed trajectories to determine a collision probability and/or additional weights or adjustment factors associated with the collision prediction, and may determine actions for the vehicle to take based on the collision predictions and probabilities.

A processor of a route providing device provided in a server according to an embodiment of the present invention: at the time of entry into a cruise control mode for causing a vehicle to travel at a speed configured by a user, causes the vehicle to travel at the configured speed in a first lane in which the vehicle is current travelling; and, when another vehicle, which is travelling at a speed slower than the configured speed in front of the vehicle, is sensed through a sensor provided in the vehicle, controls the vehicle in a preconfigured manner.

A driving assist apparatus including a communication unit and a microprocessor. The microprocessor is configured to perform acquiring traffic light information, position information, and rearward vehicle information including length information of a rearward vehicle, calculating an entire length from a front end of a subject vehicle to a rear end of the rearward vehicle based on the length information, deriving a driving assist information including a target vehicle speed or a target acceleration of the subject vehicle so that a group of vehicles including the subject vehicle and the rearward vehicle passes through a point where a traffic light is provided based on the traffic light information, the position information and the entire length, and transmitting a subject vehicle information indicating that the subject vehicle has a driving assist function capable of deriving the driving assist information to the rearward vehicle through the communication unit.

A control method and a control device are provided for autonomously controlling a host vehicle when turning across an oncoming lane at an intersection with a signal light being green. A determination is made as to whether or not the host vehicle can make a right or left turn while a signal light is still green at a time of the right or left turn, either while the host vehicle in the left lane is waiting in a row of vehicles to make the right turn or while the host vehicle traveling in the right lane is waiting in a row of vehicles to make the left turn at an intersection during travel under autonomous driving. The host vehicle then creeps forward to a start position for starting at a next time the signal light is green upon determining the right or left turn cannot be made.