An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a radar transceiver configured to detect one or more target vehicles proximate to the vehicle. The apparatus further includes a path prediction and vehicle detection controller configured to determine first parameters for predicting a path of the vehicle; determine second parameters for predicting the path of the vehicle; predict the path of the vehicle using a combination of the first parameters and the second parameters, where the combination is weighted based on a speed of the vehicle; identify one of the one or more target vehicles as a closest in path vehicle based on the predicted path of the vehicle; and activate at least one of a braking control and a steering control based on a proximity of the identified closest in path vehicle.

Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.

A method of autonomous vehicle control, comprising: receiving an image of a lenticular human-imperceptible marker embedded in an element of an environment that an autonomous vehicle is moving in, the marker having a pattern usable for determining positional data of the moving vehicle, the image captured using human-invisible light, analyzing the received image of the human-imperceptible marker, and controlling the autonomous vehicle based on the analyzed image of the human-imperceptible marker.

A motor-vehicle path-controlling module is arranged to model the path of the vehicle during a change in traffic lane by a Bezier curve relating a value of a parameter to a value of a lateral deviation of the vehicle from the center of a traffic lane and to a value of a time-dependent variable representative of the variation in the change of path; determine a setpoint state vector of a closed feedback loop of a path-controlling device, the loop being designed to control the motor vehicle so that it follows the path modelled by the Bezier curve, the vector being determined on the basis of the lateral deviation, of the time-dependent variable and of the parameter, and transmit the setpoint state vector to the input of the loop.

An assistance system for a vehicle capable of automated operation has a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The vehicle is located on a first lane in a vicinity of one or more neighboring vehicles, the first lane merging with a second lane at a merging trajectory location. The controller is adapted to selectively execute a leader determination module when a distance of the vehicle to the merge starting point is less than a threshold value. This includes determining an estimated arrival time of the vehicle to a merge starting point of the merging trajectory location. The controller is adapted to select a leader vehicle from the neighboring vehicles based in part on their respective estimated arrival times to the merge starting point. Operation of the vehicle is controlled based in part on the leader vehicle.

Implementations described herein relate to leveraging corresponding streams of speed readings of a vehicle generated by different speed sensors of different computing devices to automatically determine an updated tire size of tires of the vehicle. For example, while a user of the vehicle is driving, a first stream of speed readings can be generated by a vehicle speed sensor of an in-vehicle computing device of the vehicle and a second stream of speed readings can be generated by a mobile speed sensor of a mobile computing device of the user of the vehicle. Processor(s) can obtain the different streams of speed readings from the different computing devices and process the different streams using various operations to determine the update tire size of the tires of the vehicle. The updated tire size can be subsequently utilized to update operational parameter(s) of the vehicle that influence how the vehicle operates.

An apparatus for providing a traveling in a vehicle is provided. The apparatus includes a plurality of sensors configured to obtain information about the vehicle and information about an external object, a steering device, an input device configured to receive a lane change command from a driver of the vehicle, and a control circuit configured to be electrically connected with the one or more sensors, the steering device, and the input device. The control circuit is configured to control the vehicle to travel along a deviated path in a driving path of the vehicle based on at least one of the information obtained by the plurality of sensors or an operation of the steering device, to complete a lane change, and to control the vehicle to travel along a deviated path in a target lane of the changed lane in response to the received lane change command.

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 system for controlling vehicle functions based on monitored driving performance includes one or more sensors that capture performance-based data characterizing driving-performance implicating behaviors of a driving team during one or more driving campaigns. The performance-based data is analyzed according to one or more performance indicators to determine a compatibility score for the driving team that characterizes how driving team composition affects driving performance. One or more vehicle functions can then be controlled one or more vehicle functions based on compatibility scores for various driving teams.