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.

A method for calculating a lateral position of an ego motor vehicle on a traffic lane includes calculating a first theoretical lateral position of the ego vehicle, calculating a second theoretical lateral position of the ego vehicle, calculating a third theoretical lateral position of the ego vehicle, calculating the lateral position of the ego vehicle using a weighted average of the first lateral position, the second lateral position, and the third lateral position.

A safety system (200) for a vehicle (100) is provided. The safety system (200) may include one or more processors (102). The one or more processors (102) may be configured to control a vehicle (100) to operate in accordance with the predefined stored driving model parameters, to detect vehicle operation data during the operation of the vehicle (100), to determine whether to change predefined driving model parameters based on the detected vehicle operation data and the driving model parameters, to change the driving model parameters to changed driving model parameters, and to control the vehicle (100) to operate in accordance with the changed driving model parameters.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

A speed of a target vehicle in a target lane of operation is determined relative to a host vehicle in a host lane of operation. A virtual boundary is determined around the target vehicle based on the speed of the target vehicle. A position in the target lane and outside the virtual boundary is selected based on a) a first cost function for a deviation of a speed of the host vehicle from a requested speed, and b) a second cost function for a frequency of lane changes. Upon determining to move the host vehicle from the host lane to the target lane, the host vehicle is operated to the position in the target lane.

A vehicle includes a system and method of modeling and controlling a traction of a wheel of the vehicle. The system includes an observer, a predictive controller and an online solver. The observer receives a dynamic model parameter of the wheel and determines an estimate of a wheel velocity and an uncertainty in the wheel velocity using a non-linear model of the wheel. The predictive controller determines an average gain and differential gain from the estimate of the wheel velocity and the uncertainty in the wheel velocity. The online solver calculates a motor torque and a wheel brake torque for increasing the traction of the wheel with a road based on the average gain and the differential gain. The motor torque and the wheel brake torque are applied at the vehicle.

A method and apparatus for providing a human-machine interface (HMI) mode of a vehicle are provided. The method, performed by the device of the vehicle, for providing a human-machine interface (HMI) mode includes, analyzing a state of an occupant, calculating a confidence score for the vehicle based on the state of the occupant, determining an HMI mode corresponding to the confidence score among a plurality of predefined HMI modes; and providing first guidance information to the occupant based on the determined HMI mode.

Controlling autonomous vehicle (AV) functions includes receiving occupant identification data from device(s) of an AV and identifying occupant(s) of the AV based on the received occupant identification data, determining a respective prioritization level for each occupant of the occupant(s), the prioritization level for each occupant dictating priority of the AV in performing commands provided to the AV by that occupant to control AV functions, receiving, from an occupant, input of a command for performance by the AV to control a function of the AV, determining whether to perform the command to control the function of the AV based on the prioritization level for the occupant, and performing processing based on the determining whether to perform the command.

A computer for managing the drive train of a hybrid vehicle including an internal combustion engine, an electric machine and a battery. The drive train being capable of operating in a plurality of charging or discharging modes of the battery, the computer determines a set of probabilities of activation of the mode, determines the value of the speed of the electric motor for each mode, determines a set of electrical powers of the electric machine, calculates an energy consumption reduction indicator, determines the torque requested by the driver, the value of the speed of the internal combustion engine and the speed of the vehicle, determines a torque to be applied to the electric machine, and sends a command to the electric machine on the basis of the torque to be applied to the electric machine determined.

A method is described and includes subsequent to an autonomous vehicle becoming immobilized, initiating a local assistance request; subsequent to the initiating, receiving local assistance input from a passenger of the autonomous vehicle; and using the local assistance input to determine an action to be taken by the autonomous vehicle to mobilize the autonomous vehicle.