A driving assistance method for a vehicle. An instantaneous speed of the vehicle and an instantaneous yaw rate of the vehicle are ascertained. An operation of self-locating of the vehicle is carried out on the basis of the ascertained, instantaneous speed and the ascertained, instantaneous yaw rate of the vehicle. To that end, an instantaneous circumferential wheel speed of one or more wheels of the vehicle is directly measured, evaluated and taken as a basis of the determination of the instantaneous speed and the instantaneous yaw rate of the vehicle.

The subject matter described in this specification is generally directed control architectures for an autonomous vehicle. In one example, a reference trajectory, a set of lateral constraints, and a set of speed constraints are received using a control circuit. The control circuit determines a set of steering commands based at least in part on the reference trajectory and the set of lateral constraints and a set of speed commands based at least in part on the set of speed constraints. The vehicle is navigated, using the control circuit, according to the set of steering commands and the set of speed commands.

A tire failure detection device includes a steering angle sensor for sensing a steering angle, a yaw rate sensor for sensing a yaw rate, and a control unit. The control unit calculates side-slip energy based on the output signal of the steering angle sensor and the output signal of the yaw rate sensor, and determines that a failure has occurred in a tire when the side-slip energy exceeds a first threshold.

Methods of and systems for adaptive cruise control (ACC) of a vehicle include a controller for the vehicle that is configured to execute a computer-implemented model predictive control and a safe spacing policy that reduces collision risk between the vehicle and both a leading vehicle and a following vehicle. The methods include sensing a speed of a leader vehicle in front of the ego vehicle and a distance of the leader vehicle from the ego vehicle, sensing a speed of a follower vehicle behind the ego vehicle and a distance of the follower vehicle from the ego vehicle, and controlling the speed of the ego vehicle to avoid collision with the leader vehicle while reducing risk of the ego vehicle being hit by the follower vehicle.

A system and a method are described. The method includes: receiving sensed input from a vehicle sensor suite; using the input, providing a first output; determining that a vehicle-lane confidence level is less than a threshold; and then instead, providing a second output, wherein the first and second outputs comprise lane-correction data, wherein the second output is determined using an estimation filter.

A method for using a closed-loop control system to control an autonomous system is disclosed, the closed-loop control system comprising an explicit Nonlinear Model Predictive Control (NMPC) framework. The method (i) computes operation parameters for the autonomous system using the closed-loop control system, the output of the explicit NMPC framework comprising the operation parameters; (ii) modifies the output of the explicit NMPC framework to consider unmeasured system states, unknown system model values, and external disturbances, to create modified operation parameters, using an extended high-gain observer (EHGO) to estimate the unmeasured system states and the external disturbances and a dynamic inverter to compute values of unknown input coefficients for a system model of the autonomous system; (iii) generates a modified output signal including the modified operation parameters; and (iv) transmits the modified output signal to control operation of the autonomous system using the modified operation parameters.

A wind data estimating apparatus includes one or more processors configured to collect vehicle information including a first acceleration, an amount of driving operation performed by a driver of a vehicle, and position information, which are obtained by sensors installed in the vehicle; classify the collected vehicle information by an area of a plurality of areas according to the position information; and estimate a wind velocity and a wind direction for the area and for a time range when the vehicle information is obtained, on the basis of an acceleration obtained from subtracting a second acceleration caused by the amount of driving operation from the first acceleration included in the vehicle information classified by the area.

A method of determining operational performance parameters of a device (e.g., of a vehicle) with device mounted sensors and computer-implemented models. Further, a system, such as a virtual sensor applied to a device, such as a vehicle, for determining operational performance parameters of the device is provided. The system includes device mounted sensors and at least one processing unit configured to execute the computer-implemented method to generate an output parameter set. Measured data may be combined, and calculated parameters may be provided to a Kalman-filter to enable virtual sensing of unobservable parameters.

A target vehicle, for example a two-wheeled vehicle, for mounting onto an ADAS (Advanced Driver Assistance System) testing platform is provided. The target vehicle comprises one or more sensors and an actuation assembly comprising an actuator. The sensors are arranged to measure a parameter relating to the dynamics of the target vehicle and may for example comprise accelerometers. The actuation assembly adjusts the tilt of the target vehicle in dependence on the output of the sensor(s), for example by means of a control unit. The tilting of the vehicle during cornering may thus be simulated. The measuring of such a parameter and the adjusting of the tilt may be conducted remotely from the testing platform. The sensor(s), control unit and actuator assembly may be self-contained within the target vehicle. A method of modeling a VRU (Vulnerable Road User) for ADAS testing is also provided.

An evaluation device (10) for an interconnection of at least one first control circuit and one second control circuit for incorporating an interference signal (w), wherein the interconnection comprises at least one first controller (A) for regulating a first control variable (yA) on the basis of a first steering signal (sA) in the first control circuit, and at least one second controller (B) for regulating a second control variable (yB) on the basis of a second steering signal (sB) in the second control circuit, wherein the first steering signal (sA) of the first controller (A) comprises a second output signal (uB) of the second controller (B), comprising an input interface (11) for receiving an interference signal (2), wherein the evaluation device (10) is configured to determine at least one first model steering signal (wA) for the first controller (A) and a second model steering signal (wB) for the second controller (B) based on the interference signal (w), and at least one output interface (12) for incorporating the first model steering signal (wA) in the first steering signal (sA) and the second model steering signal (wB) in the second steering signal (sB) such that the first steering signal (sA) comprises a portion of the interference signal (w) and the second steering signal (sB) comprises a portion of the interference signal (w), in order to take into account the interference signal (w) as a steering signal when regulating a technological process.