An apparatus for providing a route of an electric vehicle and a method thereof are provided. The apparatus includes a processor that estimates a weight of a vehicle when guiding a user along a route to a destination, calculates a driving load for each route section using the estimated weight of the vehicle, calculates a driving force using motor torque, and determines a probability of hill climbing for each route section using the driving force and the driving load and a display that is controlled by the processor to display at least one of a driving load for each route section or a probability of hill climbing according to a driving force for each route section.

A vehicle control apparatus configured to calculate a center of gravity six-component; calculate a tire three-component of each wheel for two or more wheels of a vehicle imposing a constraint on each wheel expressed as an inequality corresponding to upper and lower limits of the tire three-component; apply the constraint based on whether the constraint is valid or invalid for each of the wheels based on a predetermined optimum-condition for obtaining an optimum-solution under the constraint, and calculating an optimum-solution of the tire three-component of each wheel by performing a tentative-optimum-solution-calculation one or more times until the predetermined optimum-condition is satisfied; and store an application-state of the constraint when the optimum-solution satisfying the predetermined optimum-condition is obtained, and calculate the optimum-solution of the tire three-component of each wheel by using a stored value of the application-state of the constraint, in the next calculation of the optimum-solution.

The technology relates to determining whether a vehicle operating in an autonomous driving mode is experiencing an anomalous condition, for instance due to a loss of tire pressure, a mechanical failure, or a shift or loss of cargo. The actual current pose of the vehicle is compared to an expected pose of the vehicle, where the expected pose is based on a model of the vehicle. If a pose discrepancy is identified, the anomalous condition is determined from information associated with the pose discrepancy. The vehicle is then able to take corrective action based on the nature of the anomalous condition. The corrective action may include making a real-time driving change, modifying a planned route, alerting a remote operations center, or communicating with one or more other vehicles.

A method for controlling wheel slip of a vehicle. The vehicle comprises at least a first and a second motion support device, MSD, for providing torque to a common wheel of the vehicle. The method comprises receiving a wheel torque request. Based on the received wheel torque request, the method further comprises controlling the first MSD to provide torque to the wheel in a first mode of operation, and controlling the second MSD to provide torque to the wheel in a second mode of operation which is different from the first mode of operation. The controlling of the first MSD and the controlling of the second MSD are, at least temporarily, performed simultaneously.

An image captured by a far-infrared camera is analyzed to analyze a distribution of a road-surface temperature, and a course of a highest road-surface temperature is determined to be a traveling route. Further, automatic driving along the course of the highest road-surface temperature is performed. Furthermore, a state of the distribution of a road-surface temperature, and a direction of the course of the highest road-surface temperature are displayed on a display section, so that a user (a driver) recognizes them. For example, a state analyzer detects a candidate course travelable for a vehicle, the state analyzer detecting a plurality of the candidate courses, calculates an average value of a road-surface temperature of each of the plurality of the candidate courses, and determines the candidate course having a largest average value of a road-surface temperature to be a traveling route.

The techniques of this disclosure relate to a vehicle lateral-control system with dynamically adjustable calibrations. The system includes a controller circuit that receives weather data for a geographic region traveled by a vehicle. The controller circuit receives image data from a camera of a roadway traveled by the vehicle in the geographic region. The controller circuit receives vehicle-state data from one or more vehicle sensors indicating an operating condition of the vehicle on the roadway in the geographic region. The controller circuit adjusts a calibration of vehicle lateral-control features based on the weather data, the image data, and the vehicle-state data. The controller circuit operates the vehicle on the roadway based on the adjusted calibration. The system can increase passenger comfort or reduce an error from a lane centerline when the vehicle is operated in an autonomous-driving mode, which can improve operational safety.

The invention relates to a control unit for controlling torque applied to a vehicle wheel provided with a tyre, wherein the control unit comprises or is operatively connected to a data storage, which data storage has a stored tyre model for the tyre, wherein, in the tyre model, longitudinal tyre force is represented as at least a function of longitudinal wheel slip, longitudinal wheel slip being dependent on rotational speed of the wheel and velocity of the vehicle. The control unit is configured to correct said function based on a tyre parameter input and to convert a wheel torque request to a wheel rotational speed request based on the corrected function, and to send the wheel rotational speed request to an actuator for providing a rotational speed of the wheel corresponding to said wheel rotational speed request. The invention also relates to a method and to a kit.

A vehicular control system includes an electronic control unit disposed at a vehicle, a camera disposed at a windshield of the vehicle and viewing at least forward of the vehicle through the windshield. Responsive to an emergency driving condition occurring while the vehicle is traveling along a section of a road, the system at least partially controls driving of the vehicle along the road. The system, while controlling driving of the vehicle along the road responsive to the emergency driving condition, and responsive to determination that the road changes, determines a target stopping location ahead of the vehicle. The system determines the target stopping location responsive at least in part to processing of image data captured by the camera. The system, responsive to determination of the target stopping location, at least partially controls driving of the vehicle to the target stopping location.

The technology employs a variable motion control envelope that enables an on-board computing system of a self-driving vehicle to estimate future vehicle driving behavior along an upcoming path, in order to maintain a desired amount of control during autonomous driving. Factors including intrinsic vehicle properties, extrinsic environmental influences and road friction information are evaluated. Such factors can be evaluated to derive an available acceleration model, which defines an envelope of maximum longitudinal and lateral accelerations for the vehicle. This model, which may identify dynamically varying acceleration limits that can be affected by road conditions and road configurations, may be used by the on-board control system (e.g., a planner module of the processing system) to control driving operations of the vehicle in an autonomous driving mode.

The invention relates to a method for assisting a driver of a vehicle (1) having an electric drive, in which a list of predefined influencing variables for the consumption of electrical energy by the vehicle (1) is drawn up and output by an output device (14), with the influencing variables relating to factors which can be influenced by the driver of the vehicle (1), the method comprising the following steps: a) calling up characteristic maps which specify a relationship between energy consumption and the various influencing variables, b) determining possible optimizations of the energy consumption by modifying a particular influencing variable, c) computing possible energy savings on implementation of the possible optimizations of the particular influencing variable using the characteristic maps retrieved, d) sorting the influencing variables in the list.