An arithmetic device includes a first arithmetic chip configured to perform arithmetic processing for realizing driving assistance functions of a vehicle, and a second arithmetic chip configured to perform arithmetic processing for realizing the driving assistance functions of the vehicle, wherein, among the driving assistance functions, second functions realized by the first arithmetic chip along with the second arithmetic chip includes a function of providing at least some of first functions realized without depending on arithmetic processing of the second arithmetic chip in a state in which a task imposed on a driver has been reduced, as compared to the first functions.

A method, medium, and apparatus for educating and reducing risk to inexperienced drivers using vehicles with autonomous navigation systems. Data regarding a driver's past experience with vehicles and operating environments may be used to proactively warn the driver about a potential danger detected or predicted by the vehicle. An autonomous vehicle may prevent the driver from operating the vehicle under unfamiliar circumstances or from causing a collision. Data regarding a driver's past experience with vehicles and the safety features thereof may be used to mitigate risk of injury or property damage by selectively activating safety features in a new vehicle which the driver has not previously driven. Data regarding a driver's past experience with vehicles and safety features thereof may be used to determine a decreased or increased rental rate for a particular vehicle.

Methods and systems for adapting an advanced driver assistance driving system of a vehicle. One system includes an electronic processor of an advanced driver assistance driving system. The electronic processor is configured to control the vehicle using a control parameter of the advanced driver assistance driving system. The electronic processor is also configured to activate a learning mode for the advanced driver assistance driving system and receive feedback associated with the control of the vehicle. The electronic processor is also configured to adjust the control parameter of the advanced driver assistance driving system based on the feedback and control the vehicle using the adjusted control parameter.

The disclosure includes embodiments for managing vehicles under anomalous driving behavior. In some embodiments, a method includes determining, by the processor, an ego behavior associated with the ego vehicle and a remote behavior associated with a remote vehicle. The method includes calculating a variance between the ego behavior and the remote behavior. The method includes determining a presence of an anomaly based on the variance satisfying a threshold, wherein the satisfying the threshold indicates the ego behavior is incompatible with the remote behavior. The method includes generating a driving management plan which is configured to mitigate the anomaly and is consistent with the ego behavior. The method includes implementing the driving management plan so that the threshold is no longer satisfied.

A method for outputting recommendations for energy efficient operation of a vehicle having at least two modes of operation, from which an operating mode is respectively selected by a drive controller, depending on the occurrence of specified triggers, for operating the vehicle. A change of operating mode caused by the trigger is determined. A frequency of the change of operating mode is incremented at every determination of the change of operating mode caused by the trigger. The frequency is analyzed by comparing the frequency of the change of operating mode a predetermined value. A message is generated on a case-by-case basis. The message is output via at least one output comprised by the vehicle.

This application provides a method and an apparatus for planning a vehicle trajectory, an intelligent driving domain controller, and an intelligent vehicle. One example method includes: An intelligent driving domain controller of a first vehicle obtains a first trajectory of the first vehicle, obtains a second trajectory of at least one second vehicle based on a first communications technology, and then determines trajectory planning of the first vehicle based on the first trajectory and the second trajectory of the at least one second vehicle.

This application discloses an intelligent vehicle control method. An intelligent vehicle control system obtains a driving mode, a driving style model, and a target speed of an intelligent vehicle at a current moment, then determines a speed control instruction based on the driving mode and the driving style model, and sends the speed control instruction to an execution system of the intelligent vehicle. This provides an intelligent vehicle control method with high comfort and good experience.

A determination device that determines a tendency of a driver who selects a parking space includes an acquisition unit configured to acquire information regarding an attribute of the parking space in a parking lot, information regarding behavior of a vehicle driven by the driver in the parking lot, and information regarding behavior of the driver when the driver is driving the vehicle in the parking lot, and a determination unit configured to determine the tendency of the driver when the driver selects the parking space, based on the information regarding the behavior of the vehicle or the information regarding the behavior of the driver and the information regarding the attribute of the parking space.

A method for training a style encoder of a neural network. Sensory input variables, which represent a movement of a system and surroundings of the system, are compressed to an abstract driving situation representation in at least one portion of a latent space of the neural network, using a trained situation encoder of the neural network. The sensory input variables are compressed to a driving style representation in at least one portion of the latent space, using the untrained style encoder. The driving style representation and the driving situation representation are decompressed from the latent space to output variables, using a style decoder of the neural network. A structure of the style encoder is changed to train the style encoder until the output variables of the style decoder represent the movement.

Provided is a driving evaluation device that enables evaluation of driving for the purpose of improving a driving time in a circuit racecourse. The driving evaluation device includes an evaluation section extractor 24 that detects a deceleration section and an acceleration section in cornering, a tire friction circle-load factor calculator 27 that calculates, based on a front-rear acceleration and a lateral acceleration, a tire friction circle and a tire load factor in the deceleration section and those in the acceleration section, and a skill deficiency evaluator 28 that evaluates driving based on the tire friction circle and the tire load factor in the deceleration section and those in the acceleration section.