An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

In one embodiment, a plurality of obstacles is sensed in an environment of an automated driving vehicle (ADV). One or more representations are formed to represent corresponding groupings of the plurality of obstacles. A vehicle route is determined in view of the one or more representations, rather than each and every one of the obstacles individually.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.

A method predicts the trajectory of an ego vehicle travelling in a main lane. A lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimate of the dynamic behavior of a group of vehicles travelling in the adjacent lane. The group of vehicles includes at least one main vehicle which is located near the ego vehicle and a secondary vehicle which is located behind the ego vehicle.

Disclosed are distributed computing systems and methods for controlling multiple autonomous control modules and subsystems in an autonomous vehicle. In some aspects of the disclosed technology, a computing architecture for an autonomous vehicle includes distributing the complexity of autonomous vehicle operation, thereby avoiding the use of a single high-performance computing system and enabling off-the-shelf components to be use more readily and reducing system failure rates.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

A method of determining a drivable space trajectory of an ego vehicle is described. The method includes determining a set of vehicle trajectories corresponding to a same semantic driving maneuver during motion planning of the ego vehicle. The method also includes identifying the drivable space trajectory to perform the same semantic driving maneuver. The method further includes performing a vehicle control action to maneuver the ego vehicle along the drivable space trajectory.

A vehicle control device, a vehicle control method, and a vehicle control system according to the present invention obtain an inter-vehicle time based on a relative distance between a first vehicle traveling, in front of an own vehicle, in a second lane adjacent to a first lane in which the own vehicle travels and a second vehicle traveling in the second lane in front of the first vehicle and based on a relative velocity of the first vehicle relative to the second vehicle, obtain a relative acceleration of the first vehicle relative to the second vehicle, set the first vehicle as a high-stress vehicle based on a lane change space that is based on the inter-vehicle time, the relative acceleration, and a relative distance between the second vehicle and a third vehicle traveling in the first lane in front of the own vehicle, and output a control command for changing a driving state of the own vehicle based on a relative distance between the high-stress vehicle and the own vehicle. This makes it possible to improve the driving safety of a vehicle on a road with multiple lanes in each direction.