The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

Systems and techniques are described for detecting a lane departure event. In aspects, a method includes obtaining a lateral acceleration value of the vehicle, determining a time-to-lane boundary (TTLB) threshold value using a saturated linear function of the lateral acceleration value of the vehicle, determining a current TTLB value of the vehicle with respect to a lane boundary, comparing the current TTLB value to the TTLB threshold value, and outputting a signal indicative of vehicle proximity to the lane boundary if the current TTLB value satisfies a triggering condition with respect to the TTLB threshold value.

A vehicle includes a navigation device, a sensor device, and a control device configured to identify an entry of the vehicle into a predetermined section of a road through the navigation device. In particular, the control device may identify at least one of a curvature of the road, a speed of the vehicle, or an acceleration of the vehicle through the sensor device in response to the entry of the vehicle into the predetermined section, and perform a lateral control of the vehicle based on at least one of the curvature of the road, the speed of the vehicle, or the acceleration of the vehicle.

An information processing apparatus acquires vehicle state information for each of a plurality of vehicles. The information processing apparatus estimates road surface state information of a road surface on which each of the vehicles has traveled, based on the acquired vehicle state information for each of the vehicles. The information processing apparatus estimates the road surface state information by inputting the acquired vehicle state information to a trained model that outputs the road surface state information in a case where the vehicle state information is input and that has been trained in advance based on training data in which the vehicle state information and the road surface state information are associated with each other.

An embodiment driving assistance system for a vehicle includes a driving information provision unit configured to acquire and provide driving information of a traveling vehicle, a control unit configured to generate and output a control signal for driving assistance when it is determined the vehicle travels on a rough road based on the driving information of the vehicle provided by the driving information provision unit and it is determined that the vehicle is currently in a rough road traveling state, and a steering actuator configured to generate and apply a steering assistance force according to a control value of the control signal for the driving assistance output by the control unit to a steering wheel.

A vehicle includes a powertrain having an electric machine and a controller. The controller is programmed to, responsive to an accelerator pedal position exceeding a first threshold for a predetermined time period or a lateral acceleration of the vehicle being greater than a second threshold, transition the powertrain from a nominal driving mode to a performance driving mode. The controller is also programmed to, responsive to an increase in a steering wheel angle while in the nominal mode, maintain a power output of the electric machine at a driver demanded power defined by the accelerator pedal position. The controller is further programmed to, responsive to an increase in a steering wheel angle while in the performance driving mode, reduce a power output of the electric machine to less than the driver demanded power.

Disclosed herein is an apparatus for predicting movement of a user of a vehicle. The apparatus may include an acceleration sensor that senses an acceleration of the vehicle, a braking controller that automatically controls a deceleration of the vehicle, a steering controller that automatically controls a direction of the vehicle, and a control circuit electrically connected to the acceleration sensor, the braking controller, and the steering controller, where the control circuit may monitor an operation of the braking controller, and predict a movement of the user of the vehicle based on a longitudinal acceleration of the vehicle sensed by the acceleration sensor and a first predetermined parameter when braking by the braking controller is detected.

A road friction coefficient of a vehicle is estimated by obtaining substantially contemporaneous values associated with a steering angle for a steered axle of the vehicle, a lateral acceleration, a yaw acceleration, an alignment torque and an axle load on the steered axle; estimating a lateral tire force on the basis of the steering angle, lateral acceleration, and yaw acceleration; deriving a pneumatic trail from the alignment torque and estimated lateral tyre force; and estimating a road friction coefficient from the lateral tire force, the axle load, and the pneumatic trail. In embodiments, the derivation of the road friction coefficient includes evaluating a nonlinear function of the pneumatic trail.

A method for generating a vehicle trajectory by optimizing a performance measure J. The trajectory may include a sequence of states x=(x.sub.k).sub.k=1.sup.N of the vehicle. The optimization is subject to predefined vehicle dynamics x.sub.k+1=f(x.sub.k, u.sub.k), where u.sub.k is a control input to the vehicle, and a condition that each position of the vehicle shall be close to a reference path X.sup.r. The vehicle's position is constrained inside a variable-width corridor around the reference path. A quantity r controlling the width of the corridor is included as an additional optimization variable and the performance measure includes a penalty on the corridor width. To define the corridor, each point of the reference path may be associated with a pair of laterally spaced ellipses and requiring each vehicle position to be outside the ellipses.

The invention is a method of characterizing a road for at least one route travelled by at least one road user, with sensors: a three-axis accelerometer (ACC) and a geolocation sensor (GPS). The method comprises a measurement step (MES), a measurement processing step for disregarding (AFF) the effects related to the road user's speed in order to determine vibrations due to the road roughness, and an analysis of the vibrations to characterize (CAR) the condition of the road.