A method for controlling a vehicle travelling along a route is provided. The method includes obtaining a first value of a balance parameter, indicative of a balance between a cost for operating the vehicle along at least a part of the route, and a time of arrival at a position along the route, establishing, in dependence on the first balance parameter value, a plurality of desired position and time correlations for the travel of the vehicle along at least a part of the route, when the vehicle travels along the route, determining a progress deviation comprising a deviation, for a point in time, of an actual position of the vehicle from a desired position according to the desired position and time correlations, or a deviation, for a position of the vehicle, of an actual point in time from a desired point in time according to the desired position and time correlations, when the vehicle travels along the route, obtaining a second balance parameter value, different from the first balance parameter value, the second balance parameter value being dependent on the progress deviation, and controlling the vehicle in dependence on the second balance parameter value.

An apparatus and a method are configured for estimating a slope angle of a road. The slope angle of a road is estimated based on a longitudinal slope angle of the road which is calculated based on a Kinematic model, an effective weight corresponding to a control amount of a driver and a filter constant to estimate the slope angle of the road with higher precision in the case of a U-turn, rapid acceleration, or rapid deceleration in which a longitudinal acceleration of the vehicle is remarkably increased. The apparatus includes a controller to estimate the slope angle of the road, based on a longitudinal slope angle of the road which is calculated based on a Kinematic model, an effective weight corresponding to a control amount of a driver and a filter constant, and an output device to output the estimated slope angle of the road.

A method for determining the values of parameters for a controller of a vehicle, wherein respective error measures are calculated for different sets of values and a set of values is selected based on the error measures.

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 and a system for vehicle sideslip angle estimation based on event-triggered state estimation are provided. The method includes: setting an observation variable, and adopting a Kalman filtering algorithm for iteration aiming at estimation of a vehicle yaw rate; in the iteration, setting a parameter to 1, indicating that an event is triggered, when a calculation of the observation variable satisfies a predetermined condition, otherwise setting the parameter to 0, indicating that the event is not triggered; when the parameter is set and a GPS heading angle is updated, estimating a state variable according to a formula, and calculating a vehicle sideslip angle according to the vehicle yaw rate obtained by adopting the Kalman filtering algorithm; and when the parameter is 1 and the GPS heading angle is not updated yet, estimating the vehicle sideslip angle by fusing measurement data of both a GPS and an IMU.

A path tracking method is provided. The path tracking method includes obtaining a current location and a current traveling direction of the mobile vehicle, and determining a traveling path along which the mobile vehicle travels to a destination based on the current location and the current traveling direction; simplifying the mobile vehicle into a differential model, establishing a forward objective function of the differential model in the traveling path, and obtaining an optimal solution of the forward objective function; and controlling a speed of the mobile vehicle corresponding to the differential model by using the optimal solution as an optimal differential control variable, until the mobile vehicle reaches the destination. The path tracking method is simple in calculation, adopts algorithms that is naturally stable, and is used in combination with integrated navigation, thereby ensuring stability and reliability of tracking.

A method of operating a vehicle, comprising: receiving ambient air information; receiving size, distance and relative velocity information about a vehicle in proximity to the vehicle; receiving road surface properties information; receiving wind velocity and direction information; computing an air density ratio factor using the ambient air information; computing an aerodynamic drag ratio factor using the size, distance and relative velocity information; computing a rolling resistance ratio factor using the information road surface properties information; computing effective velocity of the vehicle using the wind velocity and direction information; combining at least one of the air density ratio factor, the aerodynamic drag ratio factor and the rolling resistance ratio factor with vehicle loss coefficients to determining new vehicle loss coefficients; computing an energy loss or power loss of the vehicle using the new vehicle loss coefficients and the effective velocity of the vehicle; and controlling the vehicle to improve fuel economy.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a following distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a host vehicle braking distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a target vehicle braking distance, based on a speed and maximum braking capability of the target vehicle; and implement the planned navigational action when the predicted following distance is greater than a minimum safe longitudinal distance based on the determined host vehicle braking distance and the determined target vehicle braking distance.

A method for determining a slip angle λ.sub.r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control is provided. The slip angle λ.sub.r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle λ.sub.r1, λ.sub.r2 or λ.sub.r3 is determined by one of the three steps representing the slip angle λ.sub.r or the slip angle λ.sub.r is determined from at least two of the slip angles λ.sub.r1, λ.sub.r2 and λ.sub.r3.

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.