A method for configuring a perception component of a vehicle having one or more sensors includes generating a first set of training data that includes first sensor data corresponding to a first setting of one or more sensor parameters, and an indicator of the first setting. The method also includes generating a second set of training data that includes second sensor data corresponding to a second setting of the sensor parameter(s), and an indicator of the second setting. The method further includes training the perception component, at least by training a machine learning based model using the first and second training data sets. The trained perception component is configured to generate signals descriptive of a current state of the vehicle environment by processing sensor data generated by the sensor(s), and one or more indicators indicating which setting of the sensor parameter(s) corresponds to which portions of the generated sensor data.

Systems, methods, and computer-readable media are disclosed for controlling one or more vehicles with the use of navigation markers positioned or integrated into a ground surface. A vehicle, such as an autonomous vehicle, may include a light detection assembly, which may include a light emitter, an optical filter, an optical sensor, and an analog-to-digital converter, and optionally may include a lens. The light emitter may emit light towards the ground surface which may illuminate the navigation marker and cause the navigation marker to emit light passes through the optical filter and is ultimately sensed by the optical sensor. The vehicle may determine the light was emitted by the navigation marker and cause the vehicle to perform the predetermined action.

A reinforcement learning algorithm-based predictive control method for lateral and longitudinal coupled vehicle formation includes S1, combining a 3-DOF vehicle dynamics model that takes into account a nonlinear magic formula tire model with a lane keeping model and establishing a vehicle formation model; S2, constructing a distributed control framework and designing a local predictive controller for each following vehicle based on the vehicle formation model under the control framework; S3, using a reinforcement learning algorithm to solve the optimal control strategy of the local predictive controller, and applying the optimal control strategy to the target following vehicle. The present application completes the lateral and longitudinal coupled modeling of vehicle formation and considers the nonlinear characteristics of tires. In addition, the present application also transforms the global optimization problem of vehicle formation into a local optimization problem of each following vehicle.

A system and to a method executed in a vehicle control unit for controlling wheel slip of a vehicle, wherein the vehicle comprises at least two wheels driven by at least primary actuator via an open differential. The primary actuator is controlled to rotate at a speed resulting in a slip .sub.em of the primary actuator. A signed wheel slip limit .sub.lim is determined by adding a configurable value to the slip .sub.em of the primary actuator, such that .sub.lim>.sub.em. The at least two wheels are controlled to rotate at wheel speeds resulting in respective wheel slips .sub.l, .sub.r below the signed wheel slip limit .sub.lim, wherein each one of .sub.l, .sub.r and .sub.em are signed numerical values.

The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

A method for providing driving assistance by detecting and warning against areas on one or other side of the road which are obscured by vehicles in other lanes is based on a HD map and includes acquiring location and driving speed of a vehicle which is carrying an apparatus applying the method. The system of the method includes at least one sensor, and environmental information as to surroundings is acquired with location. The speeds of other vehicles relative to the driving speed of the vehicle are calculated, and an instruction to the driver is generated the speed of the vehicle is less than a first predefined value but the speed of the vehicle relative to the driving speeds of the other vehicles is larger than a second predefined value. The apparatus applying the method is also disclosed.

A vehicle driving assist apparatus acquires a collision index value which represents a magnitude of a collision of an own vehicle and a dozing level of a driver of the own vehicle, and executes a secondary collision reducing control of executing a forcibly-decelerating process of forcibly decelerating the own vehicle when a light collision condition is satisfied, and a dozing condition is satisfied. The vehicle driving assist apparatus executes the forcibly-decelerating process so as to decelerate the own vehicle with controlling a deceleration of the own vehicle such that the deceleration of the vehicle realized when the light collision condition and the dozing condition become satisfied, and the dozing level is relatively low, is smaller than the deceleration of the own vehicle realized when the deceleration when the light collision condition and the dozing condition become satisfied, and the dozing level is relatively high.

An apparatus in a host vehicle evaluates neighboring vehicles on a roadway for problems relating to braking performance. A plurality of remote sensors are configured to generate sensor data indicative of abnormalities of the brakes of the neighboring vehicles. A control circuit is configured to process the sensor data to identify a neighboring vehicle exhibiting an abnormality. A communication circuit wirelessly transmits a message to the neighboring vehicle conveying the abnormality. The host vehicle may include a driver assistance system responsive to the abnormality to initiate an evasive maneuver of the host vehicle in order to avoid a path of the neighboring vehicle which exhibits the abnormality.

Systems and methods for controlling a vehicle. The systems and methods receive static object detection data from a perception system. The static object detection data includes a first representation of a static object at a current time and a second representation of the static object at an earlier time. The systems and methods receive vehicle dynamics measurement data from the sensor system, determine a current position of the static object based on the first representation of the static object, predict an expected position of the static object at the current time using the second representation of the static object at the earlier time, a motion model and the vehicle dynamics measurement data, estimate a lateral velocity of the vehicle based on a disparity between the current position and the expected position, and control the vehicle using the lateral velocity

A slip determination system is provided that is capable of providing appropriate control information to a traveling vehicle when the traveling vehicle has proceeded to an area where a slip is likely to occur during automatic travel. The slip determination system includes: a vehicle position detection module for detecting a vehicle position; and an automatic travel control portion for enabling automatic travel based on the vehicle position and a set travel path; a slip amount calculation portion for calculating a slip amount of the traveling vehicle body, using an estimated movement distance of the traveling vehicle body calculated based on the number of revolutions of a driving axle of the traveling vehicle body, and an actual movement distance of the traveling vehicle body calculated based on the vehicle position; an appropriateness determination portion for performing appropriateness determination to determine, based on the slip amount, whether or not a state of a traveling ground surface is appropriate for automatic travel; and an automatic travel stop portion for stopping automatic travel based on a determination result.