A prediction method and apparatus for an autonomous driving manual takeover, and a system are provided. One example method includes: A first vehicle sends a first message to a second vehicle when detecting that the first vehicle has a manual takeover requirement, where the first message includes information about a first location of the first vehicle, and the information about the first location is used to indicate a location of the first vehicle when the first vehicle detects that the first vehicle has the manual takeover requirement.

A method for deactivating an automated driving mode of a vehicle involves automatically terminating the automated driving mode if, due to a manual steering torque acting on the steering wheel of the vehicle, a system steering torque generated by a control system of an assistance system for automated driving mode in terms of amount is exceeded by a predetermined first value if at least one hand of a vehicle user is detected on the steering wheel of the vehicle, is exceeded by a predetermined second value if neither of the vehicle user's hands is detected on the steering wheel, or is exceeded by a predetermined third value if it is determined that the vehicle user is distracted from the driving situation, or if it is determined that there is a lateral collision risk for the vehicle and the manual steering torque of the vehicle user is acting in the direction of the collision risk.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

A vehicle control system uses reliability of an input signal of an autonomous vehicle to safely travel through an intersection or a crossroad. The system includes a first calculating unit that calculates reliability for behavior information of a front vehicle and a second calculating unit calculates reliability for state information of a traffic light in the crossroad or the intersection based on a surrounding vehicle. A third calculating unit calculates reliability for brake light information of the front vehicle and a fourth calculating unit calculates reliability for flow information of the surrounding vehicle passing the crossroad or the intersection. A determining unit generates a vehicle control signal according to the calculated reliability.

A sleepiness estimating device including an auxiliary information acquirer that acquires auxiliary information including at least one of five-sense information perceived by a person or emotion information indicating an emotion of the person; and a sleepiness estimator that estimates a sleepiness of the person based on the auxiliary information.

A driver assistance method and a driver assistance apparatus are provided, which may be applied to the field of autonomous driving or intelligent driving. The driver assistance method includes: determining that a driver is in an abnormal state; determining that a first operation performed by the driver on a first terminal is an abnormal operation; and performing first processing, where the first processing includes outputting indication information and/or control information, and the indication information or the control information indicates a second operation performed on the first terminal, or the first processing includes controlling the first terminal to perform the second operation.

The present teaching relates to method, system, and medium, for generating an augmented alert in a hybrid vehicle. First information indicating an upcoming switch in an operating mode of the vehicle is received, which specifies a set of tasks, arranged in an order, to be completed by a driver in the vehicle to achieve the upcoming switch, and a task duration for each of the set of tasks by which the task is to be completed. A current state of the driver is obtained and used to determine a set of warnings to alert the driver to perform the set of tasks. Each warning corresponds to a task in the set of tasks and is created based on the current state of the driver. A warning schedule is generated based on the set of warnings in the order of the set of tasks and transmitted so that warnings in the warning schedule are delivered to the driver.

A vehicle includes a detection system configured to acquire data regarding operation of the vehicle, and a robotic driving device configured to provide robotic control of the vehicle. The vehicle also includes a control system configured to determine whether the robotic driving device is activated, such that the vehicle is in robotic driving mode; receive a request by a prospective operator of the vehicle to deactivate the robotic driving device to initiate a manual driving mode; determine whether the prospective operator is impaired based on the data; and selectively grant or refuse the request based on the determination.

Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

The present invention relates to a method for alerting drivers and/or autonomous vehicles of high risk scenarios. The method includes obtaining positional data of a vehicle, where the positional data is indicative of geographical position and heading of the vehicle. The method further includes obtaining environmental data of the vehicle, where the environmental data is indicative of state of the surrounding environment of the vehicle. The method includes determining, by means of trained model, accident intensity for upcoming road portion for the vehicle, the trained model being configured to determine accident intensity associated with the upcoming road portion based on the obtained environmental data and the obtained positional data. Then, if the determined accident intensity exceeds threshold, the method comprises transmitting signal indicating approaching high risk scenario to a Human-Machine-Interface, HMI, of the vehicle and/or to a control system of the vehicle.