A vehicle control system includes a recognizer that recognizes a surrounding environment of a vehicle, and a driving controller that performs speed control and steering control of the vehicle based on a recognition result of the recognizer. When moving the vehicle to a parking area after detecting that an occupant gets off the vehicle at a stop position, the driving controller adjusts a start timing for starting the vehicle based on the number of vehicles stopped at the stop position recognized by the recognizer.

An autonomous driving system performing autonomous driving of a vehicle, includes a vehicle position acquisition device acquiring a position of the vehicle on a map, a vehicle state acquisition device acquiring a vehicle state of the vehicle, an occupant state acquisition device acquiring an occupant state of the vehicle, a destination candidate database storing destination candidate data including a destination candidate and the occupant state correlated with the destination candidate; and an electronic control unit. The electronic control unit determines whether or not the vehicle is in a boarding completion state based on the vehicle state and the occupant state, and sets a destination of the vehicle in autonomous driving based on the position of the vehicle on the map, the occupant state, and the destination candidate data in a case where electronic control unit determines that the vehicle is in the boarding completion state.

A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.

A control device controlling the movement route of a transportation vehicle calculates a movement route to a target position of the transportation vehicle and, in a case where an obstacle exists on the calculated movement route, counts the number of detours indicating the magnitude of an influence of the obstacle on movement route calculation, and calculates the movement route again so as to avoid the obstacle.

A controller of an autonomous traveling vehicle as an information processing apparatus of the disclosure executes acquiring first information relating to a predetermined item and the first information relating to predetermined processing in a processing apparatus mounted on the autonomous traveling vehicle, acquiring second information relating to shipping of the predetermined item, controlling running of the autonomous traveling vehicle based on the second information, and controlling the operation of the processing apparatus based on the first information when the autonomous traveling vehicle is in a running state based on the second information.

Provided is a method including monitoring an event occurring on a path along which an autonomous vehicle is currently travelling by using information obtained while the autonomous vehicle is travelling and determining whether to travel along a detour path when the event is detected. In particular, the method includes determining whether to travel along the detour path according to a cost incurred when the autonomous vehicle travels along the detour path.

In an information processing device, a first acquisition portion acquires a usage end time and a usage end point of an automatic driving vehicle. A second acquisition portion acquires information about a side trip point of the automatic driving vehicle. A derivation portion derives an expected time of arrival of the automatic driving vehicle at the usage end point by way of the side trip point. A permission portion permits a drop-in at the side trip point when the expected time of arrival at the usage end point is before the usage end time.

A vehicle includes a control unit. The control unit is configured to execute: outputting, when detecting or estimating travel inability of an own vehicle, information regarding a request for receiving an article provided from another vehicle to avoid the travel inability. The control unit performs, when determining that a request vehicle outputs the information regarding the request, a prescribed process for providing an article corresponding to the request to the request vehicle.

A velocity trajectory generation method and apparatus, and a storage medium are provided. The method includes: setting starting point time, intermediate point time and terminal point time of a velocity trajectory to be generated, wherein the velocity trajectory includes a first velocity sub-trajectory: and a second velocity sub-trajectory; generating multiple first velocity sub-trajectories corresponding to a starting point velocity between the starting point time and the intermediate point time, according to the starting point velocity and multiple intermediate point velocities; and generating, for each of the first velocity sub-trajectories, multiple second velocity sub-trajectories between the intermediate point time and the terminal point time according to multiple terminal point velocities. A velocity trajectory which is more in line with a human driving characteristic may be generated, the comfort level of a passenger may be improved, and an algorithm may be simplified.

Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.