The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in closed-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning development-module.

A vehicle comprising a safety device comprising a supporting member having a first end provided with a joint part which is pivotally mounted on a side wall of the vehicle, and a second end configured to bear on the ground. The vehicle further comprises an actuator connected to the supporting member and configured to move it relative to the vehicle between an inactive position in which the supporting member is located along the vehicle side wall, with the second end at a distance from the ground, and a safety position in which the supporting member extends between the vehicle side wall and the ground, to limit vehicle roll by means of the second end bearing on the ground.

In a vehicle control device, a switching hyperplane generation unit generates a switching hyperplane based on a travel state of a vehicle and cornering stiffness dependent on a travel surface state as a state of a road surface on which the vehicle travels. A deviation computation unit calculates a deviation between a target trajectory and an actual trajectory of the vehicle. A state estimation unit estimates a state to be controlled of the vehicle based on the deviation calculated by the deviation computation unit. A target steering angle and acceleration/deceleration computation unit calculates a target steering angle and a target acceleration/deceleration rate of the vehicle based on the switching hyperplane generated by the switching hyperplane generation unit and an estimated state as the state estimated by the state estimation unit.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.

Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

The present invention obtains a controller and a control method capable of appropriately stabilizing a posture of a straddle-type vehicle. In the controller and the control method according to the present invention, when the straddle-type vehicle jumps, automatic posture control for controlling the posture of the straddle type vehicle by increasing or reducing a rotational frequency of a wheel is executed in accordance with posture information at the time when the straddle-type vehicle jumps. Furthermore, in the case where it is determined whether a driver has intention to control the posture of the straddle-type vehicle at the time when the straddle-type vehicle jumps without relying on the automatic posture control and where it is determined that the driver has the intention, the automatic posture control is prohibited.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

Disclosed is an apparatus and method for estimating a road profile for a vehicle. The apparatus includes a camera, processors, and a controller. The processors are configured to obtain a state of the vehicle including a behavior of the vehicle, and obtain a road height from information provided by the camera. The controller is configured to estimate the road profile by changing coordinates of the camera according to the behavior of the vehicle based on a vehicle height, compensating for the road height and a recognition road distance, and matching and filtering multiple road heights.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.