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.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

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.

Disclosed herein is a method of controlling a terrain driving mode of a hybrid vehicle, including defining demand torque required for vehicle driving depending on driver demand and an environment of a driving road, differentiating demand torque in response to the terrain driving mode, calculating accumulated driving energy from a time point of an operation in the terrain driving mode based on the differentiated demand torque, and determining a terrain driving method based on the calculated accumulated driving energy and a state of energy (SoE) in consideration of a state of charge (SoC) and a voltage condition of a battery cell.

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

A vehicle control system having a subsystem controller for initiating control of a first group of at least one vehicle subsystem in a selected one of a plurality of subsystem control modes each corresponding to one or more different driving conditions; and an estimator module for evaluating at least one driving condition indicator to determine the extent to which each of the subsystem control modes is appropriate and for providing an output indicative of the subsystem control mode that is most appropriate. The estimator module is configured to increase the probability to which the at least one off-road driving mode is determined appropriate in dependence on at least one terrain indicator. In an automatic response mode the subsystem controller selects the most appropriate one of the subsystem control modes for each subsystem of the first group in dependence on the output.

A method for operating an electrically operated or also electrically operable motor vehicle provided with a rechargeable electric energy storage device associated with the drive motor of the motor vehicle. A target charging state is determined for the energy storage device and an operating strategy is determined for a route that is calculated, entered or predicted for the next trip, by which recuperative deceleration is enabled with a specifiable minimum amount for deceleration processes occurring along the route. A total mass of the motor vehicle, including optionally a trailer connected to the motor vehicle, deviating from an input normal value and an air resistance of the motor vehicle deviating from a predetermined normal value are taken into account.

A vehicle is provided including an electronic power steering system, an electronic throttle control system, and a stability control system.

A method for operating a control device of a motor vehicle driving by automation. The method includes determining a location of the motor vehicle, and acquiring driving-environment data of the motor vehicle, a control characteristic of the control device of the motor vehicle being formed in such a way that a driving behavior of at least one other road user is influenced in defined manner.

A control device includes a sensing unit configured to detect information regarding an environment of a vehicle; a head lamp configured to selectively output light of at least one color among a plurality of colors; and at least one processor configured to control the head lamp to selectively output light of a first color based on the information regarding the environment of the vehicle satisfying a first condition. In addition, a method for controlling a vehicle includes: detecting, through a sensing unit, information regarding an environment of the vehicle; and controlling, by at least one processor, a head lamp of the vehicle to selectively output light of a first color among a plurality of colors based on the information regarding the environment of the vehicle satisfying a first condition.