The present invention determines at least one dangerous driving indicator by use of a physical model based on the dynamics of a vehicle. According to the invention, a dynamic model of the vehicle determines a slip parameter of the vehicle, which deduces a representative dangerous driving indicator.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

A control system for a vehicle includes an internal vehicle reference model that determines reference states for the vehicle that represent an expected vehicle response, sensors that determine measured states for the vehicle, and a vehicle motion control system that determines desired states for the vehicle. A stability determining module identifies a reference deviation between the reference states and the measured states, identifies a desired deviation between the desired states and measured states, and outputs a command for reducing the reference deviation and the desired deviation. Actuators are operable to reduce the reference deviation and the desired deviation in response to the command.

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.

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.

A vehicle stability control system and a vehicle stability control method which are capable of more improving lateral stability of a vehicle when the vehicle is turning on a descent inclined road, may enable the vehicle to turn along a turning trace intended by a driver through cooperative control of active front steering (AFS) control and an electronic stability control (ESC) when the vehicle is turning on the descent inclined road.

Systems and methods are provided to enhance the driving performance of a leanable vehicle such as a motorcycle. The system includes a leanable vehicle interface to receive input from a driver (e.g., a human or a robotic driver) and a sensor interface to receive inputs from sensors on the leanable vehicle. The system also includes a computing module to use the sensor data in combination with data from the leanable vehicle interface to calculate the driver behavior to produce a future desired performance, based on a specified aggressiveness, so that the performance of the leanable vehicle is optimized. The calculation may be done using a machine learning method, a rule based method, or both.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

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.

The rolling parameters of a tire on a rolling surface are evaluated, and more specifically a tire's adhesion potential on a rolling surface is estimated. A method and a system enabling such estimation are disclosed.