A processor unit (3) is configured to execute an autonomous driving function of the motor vehicle (1) during a first instance such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function. The processor unit (3) is further configured to store a driver intervention, the driver intervention being performed by a driver of the motor vehicle (1) during the first instance while the motor vehicle (1) travels autonomously based on the execution of the autonomous driving function. Additionally, the processor unit (3) is configured to execute the autonomous driving function during a second instance, subsequent to the first instance, based at least in part on the stored driver intervention such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function according to the stored driver intervention.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

A vehicle travel assistance method executed by a processor comprises: setting a travel lane in which a subject vehicle travels based on detection information of a sensor equipped in the subject vehicle, identifying a preceding vehicle traveling ahead of a subject vehicle based on detection information of a sensor, calculating, based on the detection information, a first evaluation value indicating a possibility that the subject vehicle can return to the travel lane from an adjacent lane adjacent to the travel lane after overtaking the preceding vehicle, calculating a shortening width of travel time shortened by overtaking the preceding vehicle based on the vehicle speed of the subject vehicle and the vehicle speed of the preceding vehicle, and determining whether or not to overtake the preceding vehicle based on the first evaluation value and the shortening width.

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.

An information processing method, a server, and an intelligent mobile robot, so that when the intelligent mobile robot encounters a danger, the intelligent mobile robot exchanges information with the server to achieve a purpose of escaping from a scene to a safe place. The method includes: receiving, by a server, a danger alarm sent by an intelligent mobile robot, where the danger alarm is used to indicate that the intelligent mobile robot detects a dangerous event; determining, by the server, a safe position for the intelligent mobile robot, where the safe position is a position in which the dangerous event does not occur currently; and sending, by the server, an escape instruction to the intelligent mobile robot, where the escape instruction includes the safe position.

In an approach to improve mobile computation while traveling by dynamically generating one or more routes base on computing resource requirements of one or more endpoint devices. Embodiments identify, in real time, a plurality of autonomous vehicles, wherein the plurality of autonomous vehicles are traveling along a common route. Further embodiments, adjust, in real time, relative positions and speeds of the plurality of autonomous vehicles to maintain the plurality of autonomous vehicles within a predetermined geographic area while traveling along the common route, and wherein the predetermined geographic area is sufficient to collectively provide an amount of edge computing resources to satisfy one or more computing resource requirements of the one or more endpoint devices located within a first autonomous vehicle. Additionally, embodiments adjust, in real time, a route of the first autonomous vehicle based on the common route of the plurality of autonomous vehicles providing the edge computing resources.

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.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a prime mover (8) and of at least one vehicle component influencing energy efficiency of a motor vehicle. The MPC algorithm (13) includes a longitudinal dynamic model (14) of the drive train (7) and of the vehicle component influencing the energy efficiency of the motor vehicle (1) as well as a cost function (15) to be minimized. The cost function (15) includes at least one first term. The processor unit (3) is configured for determining a particular input variable for the prime mover (8) and for the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) by executing the MPC algorithm (13) as a function of a particular term such that the cost function (15) is minimized.

A method of motion planning and maneuvering of an ego vehicle is described. The method includes computing a set of potential vehicle maneuvers during a current motion planning cycle of the ego vehicle. The method also includes identifying, for the set of potential vehicle maneuvers, space corridor compatible vehicle maneuvers from a set of previously computed vehicle maneuvers during a previous motion planning cycle. The method further includes prioritizing a currently executed vehicle maneuver when the currently executed vehicle maneuver is a space corridor compatible vehicle maneuver with the set of potential vehicle maneuvers.