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.

The disclosure relates to a method for generating a reference trajectory within a lane for a vehicle. The method comprises receiving at least one vehicle current state parameter describing a current state of the vehicle (S11). The current state of the vehicle comprises at least a current position of the vehicle. Furthermore, a destination parameter describing a destination to be reached by the vehicle (S12), and at least one route parameter describing a route for reaching the destination (S13) are received. Moreover, the method comprises estimating a power loss being caused when traveling from the current position of the vehicle to the destination (S14). The reference trajectory within the lane is determined such that it minimizes the power loss and leads to the destination (S15). Additionally, a method for operating a vehicle is presented. According to this method, a reference trajectory is generated in accordance with the above method (S21) and at least one control signal is provided for controlling a motion of the vehicle along the reference trajectory (S22). Furthermore, a data processing apparatus, a vehicle and a computer-readable medium are presented.

A vehicle driving assistance apparatus predicts (i) a first consumed energy amount corresponding to a consumed energy amount consumed by a driving apparatus of an own vehicle when executing a first following control and (ii) a second consumed energy amount corresponding to the consumed energy amount consumed by the driving apparatus of the own vehicle when executing the second following control. The apparatus executes the second following control when the second consumed energy amount is smaller than the first consumed energy amount. On the other hand, the apparatus executes the first following control when the second consumed energy amount is equal to or greater than the first consumed energy amount.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

A vehicle information DB holds information on whether or not a vehicle falls under a welfare vehicle provided with an assistance apparatus for a driver who has a lower limb impairment. A power feed facility DB holds position information of a power feed facility and information indicating whether the power feed facility falls under a contact type power feed facility or a wireless power feed facility. A processing apparatus creates a travel route such that a larger number of wireless power feed facilities are included along the travel route when the vehicle falls under the welfare vehicle than when the vehicle does not fall under the welfare vehicle.

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.

To navigate an electric vehicle from a starting location to a destination, a system identifies multiple charging stations between the starting location and the destination and determining a navigation route that requires a least amount of time for the electric vehicle to travel from the starting location to the destination via one or more of the charging stations, including determining a non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time.

Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

The disclosure relates to a method for evaluating a driving behavior, wherein detected driving data of at least one human driver, or detected driving data of at least one automated driving vehicle are obtained, wherein a key performance indicator is determined based on the obtained driving data, wherein both a travel time as well as an energy efficiency and/or emissions efficiency are taken into account in determining the key performance indicator, wherein the determined key performance indicator is provided as an evaluation result.

The disclosure belongs to the technical field of autonomous vehicle, in particular to an automatic driving acceleration test method considering efficiency and coverage, which includes the following steps. Step 1 is definition of scenario test priority. Step 2 is zone division. Step 3 is search within zones. Step 4 is update of scenario test priorities. Step 5 is iterative test. After selecting the automatic driving function to be tested and setting the parameters of the vehicle operation zone, the scenario generation range is formed. The coverage of the test scenario is improved by dividing the generated range and setting the freedom of early autonomous driving exploration. The efficiency of the test process is improved by continuously improving the probability of generating dangerous scenarios in the test process. Thus, it is ensured that the generated test scenarios take into account both test efficiency and test coverage.