In a method and control device for providing feedback to a vehicle occupant, a signal corresponding to current and/or upcoming movements of the vehicle is obtained when the vehicle is in an autonomous driving mode. The obtained signal is provided to a control device located within the cabin of the vehicle. The control device is moved corresponding to the provided signal. In an embodiment, an input of the occupant for an autonomous driving unit of the vehicle via the control device is received and a signal corresponding to the received input is submitted to the autonomous driving unit. The input may correspond to a maneuver request and/or to a selection of one of several autonomous driving-modes.

A planning system for an autonomous work vehicle system includes a controller having a memory and a processor. The controller is configured to determine an action to be performed by the autonomous work vehicle system and an action location of the action in a work area, determine a transition operation associated with the action that enables the autonomous work vehicle system to perform the action at the action location, and generate a plan for operation of the autonomous work vehicle system. The plan includes the action, the action location, and the transition operation.

Systems and methods for controlling navigation of an autonomous vehicle for making an unprotected turn while traversing an intersection. The methods may include identifying a loiter pose of an autonomous vehicle for stopping at a point in an intersection before initiating an unprotected turn, initiating navigation of the autonomous vehicle to the loiter pose when a traffic signal is at a first state, determining whether the traffic signal has changed to a second state during or after navigation of the autonomous vehicle to the loiter pose, and in response to determining that the traffic signal has changed to the second state, generating a first trajectory for navigating the autonomous vehicle to execute the unprotected turn if the expected time for moving the autonomous vehicle from a current position to a position when the autonomous vehicle has fully exited an opposing conflict lane is less than a threshold time.

An autonomous driving system for an autonomous vehicle includes an automated driving controller wirelessly connected to a towing taxi. The automated driving controller determines the autonomous driving system is non-functional. In response to determining the autonomous driving system is non-functional, the automated driving controller generates a notification indicating the autonomous driving system is non-functional. The automated driving controller receives, from the towing taxi, a current data string including a data point corresponding to a current point in time in combination with a predicted data point for each of one or more predicted points of time in the future. The current data string is compared with a previous data string recorded at a previous point in time. In response to determining the current data string matches the previous data string, the automated driving controller determines one or more driving maneuvers for the autonomous vehicle based on the current data string.

A method of maintaining stability of a motor vehicle having a first axle, a second axle, and a steering actuator configured to steer the first axle includes determining localization and heading of the vehicle. The method also includes determining a current side-slip angle of the second axle and setting a maximum side-slip angle of the second axle using the friction coefficient at the vehicle and road surface interface. The method additionally includes predicting when the maximum side-slip angle would be exceeded using the localization, heading, and determined current side-slip angle as inputs to a linear computational model. The method also includes updating the model using the prediction of when the maximum side-slip angle would be exceeded to determine impending instability of the vehicle. Furthermore, the method includes correcting for the impending instability using the updated model and the maximum side-slip angle via modifying a steering angle of the first axle.

The present invention prevents occurrence of abnormal noise and swing of a vehicle in mitigating braking force of a steered wheel while reducing a steering load at the time of stationary steering to reduce a burden of a steering device and reducing stress accumulation due to stationary steering to reduce burdens of a tire, a suspension device and the steering device. The present invention includes a stop braking force control unit 202 that individually controls braking forces of steered wheels 51 and 52 and non-steered wheels 53 and 54 at the time of deceleration of the vehicle, and a pre-detection unit 203 that detects steering in a stopped state of the vehicle in advance, in which the stop braking force control unit executes, when the steering in a stopped state of the vehicle is detected in advance by the pre-detection unit, braking force mitigation control to decrease the braking forces of the steered wheels to be lower than the braking forces at the time of normal braking.

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 device for controlling the movement of a motor vehicle, including a longitudinal controller and a lateral controller which are capable of generating, from first information relating to the road layout and second information relating to the dynamic behaviour of the vehicle, control commands intended for actuators for controlling the longitudinal and lateral movement of the vehicle. The device includes a prediction model which is supplied with the first and second information and is capable of determining future states of the vehicle for future positions of the vehicle over a plurality of iterations defining a future road portion. The model is connected to a module for determining whether driving limit values are violated, which module is capable of determining, for each future state, whether one of the state variables defining the future state reaches or exceeds a driving limit value, and of deducing a future risk situation.

In one embodiment, an exemplary method includes receiving, at a simulation platform, a record file recorded by a manually-driving ADV on a road segment, the simulation platform including a first encoder, a second encoder, and a performance evaluator; simulating automatic driving operations of a dynamic model of the ADV on the road segment based on the record file, the dynamic model including an autonomous driving module to be evaluated. The method further includes: for each trajectory generated by the autonomous driving module during the simulation: extracting a corresponding trajectory associated with the manually-driving ADV from the record file, encoding the trajectory into a first semantic map and the corresponding trajectory into a second semantic map, and generating a similarity score based on the first semantic map and the second semantic map. The method also includes generating an overall performance score based on each similarity score.

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.