The disclosed invention makes use of slip related values to calculate friction related values and tire stiffness related values and feeds back an estimated tire stiffness relates value or a calculated friction related as a basis for further calculations. In particular, the disclosure relates to methods, apparatuses and computer program products to achieve the mentioned objective.

A system for predicting a hazardous event from road-scene data includes an electronic control unit configured to implement a neural network and a camera communicatively coupled to the electronic control unit, wherein the camera generates the road-scene data. The electronic control unit is configured to receive the road-scene data from the camera, and predict, with the neural network, an occurrence of the hazardous event within the road-scene data from the camera.

The vehicle movement control apparatus of the disclosure sets an update movement route as a target movement route when an update condition is satisfied. The apparatus acquires a turning characteristic, an acceleration characteristic, and a deceleration characteristic of a vehicle while executing an automatic movement control to cause the vehicle to move along the update movement route. The apparatus updates vehicle behavior characteristic data so as to represent actual vehicle behavior characteristics, based on the acquired turning characteristics, the acquired acceleration characteristic, and the acquired deceleration characteristic.

It is an object of the present disclosure to provide a travel path generation apparatus and a travel path generation method enabling generation of a travel path not including a curve having a small radius of curvature. A travel path generation apparatus according to the present disclosure includes: a parameter determination unit to determine a cutoff frequency based on spacing between points of a sequence of points each having location information and a predetermined threshold of a radius of curvature, the sequence of points representing a shape of a lane in which a vehicle travels in a two-dimensional or three-dimensional Cartesian coordinate system; and a filtering unit to perform low-pass filtering using the cutoff frequency determined by the parameter determination unit on the location information of each of the points of the sequence of points.

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.

A posture estimation method includes calculating a posture change amount of an object based on an output of an angular velocity sensor, predicting posture information of the object by using the posture change amount, adjusting error information in a manner of determining whether or not the output of the angular velocity sensor is within an effective range and, when it is determined that the output of the angular velocity sensor is not within the effective range, increasing a posture error component in error information and reducing a correlation component between the posture error component and an error component other than the posture error component in the error information, and correcting the predicted posture information of the object based on the error information.

Method for operating a motor vehicle (1), comprising a navigation device (2) having at least one stored item of route information describing a route section and a cruise control system (3) that adjusts an actual speed of the motor vehicle (1) to a stored target speed determined as a function of the stored item of route information by controlling a drive (5) or a braking device (6) of the motor vehicle (1), wherein the navigation device (2) is supplied with at least one changed item of route information relating to the changed route section, and the cruise control system (3) adjusts the actual speed of the motor vehicle (1) as a function of at least one evaluation parameter, on the basis of which a traversal of the changed route section (15) earlier in time is qualified, either to the stored target speed determined as a function of the stored item of route information or to a changed target speed determined as a function of the changed item of route information.

A system, apparatus and method for controlling operation of a vehicle. Driver assistance system (DAS) data may be used for controlling one or more function of the vehicle via a driver assistance system. Driver profile data (DPD) is generated and/or received that includes training data from sensor monitoring of a driver's usage characteristics for at least one feature of a vehicle type during a driver-controlled mode of vehicle driving. The DPD is processed with the DAS data to determine if the DPD conform to one or more safety metrics. A driver profile data file may be created by weighing the portions of the DAS data with the DPD that do not conform with the safety metrics. One or more functions on the vehicle may be controlled via the driver profile data file using the driver assistance system during one of a semi-autonomous and fully autonomous mode of operation.

A method and device for the combined determining of a momentary vehicle roll angle of a motor vehicle and a momentary roadway cross slope of a curved roadway section traveled by the motor vehicle is disclosed. The momentary vehicle roll angle and momentary roadway cross slope are determined from chassis data and transverse dynamics data of the motor vehicle.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.