A vehicle control device includes a recognition unit which recognizes surrounding situations of a vehicle, and a driving control unit which automatically controls at least steering of the vehicle based on the surrounding situations recognized by the recognition unit, and the driving control unit increases a distance between the vehicle and a traffic participant in the case where the recognition unit recognizes the traffic participant as an overtaking target and a predetermined structure in a traveling direction of the vehicle, and the vehicle travels on a side opposite to the predetermined structure in a road width direction in a case that viewed from the traffic participant to overtake the traffic participant, as compared to a case where the traffic participant as the overtaking target is recognized by the recognition unit in the traveling direction of the vehicle and the predetermined structure is not recognized.

Systems and methods for operating a vehicle are disclosed. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle while the vehicle is in motion; detecting an object within a given distance from the vehicle; generating at least one possible object trajectory for the object which was detected; performing a collision check to determine whether a collision between the vehicle and the object can be avoided based on the vehicle trajectory and the at least one possible object trajectory; performing a plausibility check to determine whether the collision is plausible based on content of a map, when a determination is made in the collision check that a collision between the vehicle and the object cannot be avoided; and performing operations to selectively cause the vehicle to perform an emergency maneuver based on results of the plausibility check.

A traveling control apparatus to be applied to a vehicle includes a traveling controller, a traveling environment recognizer, a target traveling course setting section, a steering assist controller, a target traveling course switching section, and an own-vehicle position and own-vehicle traveling course estimator. The own-vehicle position and own-vehicle traveling course estimator estimates a first lateral position of the vehicle, an estimated traveling course along which the vehicle is to travel after start of a lane change control, and a second lateral position of the vehicle to be reached after the vehicle travels for a predetermined time period from the first lateral position. The target traveling course switching section regards the vehicle as traveling within an adjacent lane, and switches a target traveling course from within a traveling lane to within the adjacent lane, if the two lateral positions are determined as each satisfying a relationship with a predetermined threshold.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

A trajectory setting device that sets a trajectory of a host vehicle includes a first path generation unit configured to generate a first path by assuming all obstacles around the host vehicle to be stationary obstacles, a second path generation unit configured to generate a second path when the moving obstacle is assumed to move independently, a third path generation unit configured to generate a third path when the moving obstacle is assumed to move while interacting with at least one of the other obstacles or the host vehicle, a reliability calculation unit configured to calculate reliability of the second path and reliability of the third path, and a trajectory setting unit configured to set the trajectory for traveling from the first path, the second path, and the third path based on the reliability of the second path and the reliability of the third path.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for autonomous driving with surfel maps. In some implementations, a three-dimensional representation of a real-world environment is obtained. Each of the surfels can correspond to a respective point of plurality of points in a three-dimensional space of the real-world environment. Input sensor data is received from multiple sensors installed on the autonomous vehicle. A pedestrian is detected from the input sensor data. A determination is made that the pedestrian is located behind a barrier. A driving plan is updated based on determining that the pedestrian is located behind a barrier.

Lane configuration of a lane on which an ADV is driving and a current speed of the ADV are determined. A nudge space is determined based on the lane configuration, the current speed, and a vehicle width of the ADV. Path planning is performed to generate a path to nudge an obstacle, in response to the determining that the nudge space is greater than a predetermined threshold nudge space. The ADV is controlled to drive autonomously according to the planned path to nudge the obstacle.

Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processor may be programmed to obtain images representative of an environment of a host vehicle; identify, from the images, a feature of a roadway used to navigate the host vehicle on a path, the identified feature of the roadway having an ambiguity along the path; obtain map data associated with the environment to resolve the ambiguity of the identified feature; and generate a trajectory to navigate the host vehicle on the roadway, using the map data associated with the environment.

When a vehicle performing driverless operation of encounters a blockade situation, a probable blockade time duration of the blockade situation is predicted based on a situational analysis. Support by a teleoperator is requested when the predicted blockade time duration of the blockade situation is greater than a predetermined time duration or when the vehicle has waited longer than the predicted blockade time duration for a resolution of the blockade situation.

In this vehicle control device, a low-level short-term trajectory generating unit generates a short-term trajectory by using the newest dynamic external environment recognition information while also using the same static external environment recognition information (lane shape information, or the like) as the static external environment recognition information used by a high-level medium-term trajectory generating unit. Performing such control prevents inconsistency between the external environment information (environmental information) used by the low-level short-term trajectory generating unit and that used for a medium-term trajectory, which is a high-level trajectory, and makes stable trajectory output possible.