Techniques for using ball joint sensor data to determine conditions relevant to a vehicle are described in this disclosure. For example, in one example, the ball joint sensor data may be used to determine a ride height at a portion of the vehicle, which may be used to determine roll data and/or pitch data. The ride height, roll data, and/or pitch data may be directly used to navigate through an environment, such as by the vehicle relying on the data when interpreting sensor data or planning driving operations. Also, the ride height, roll data, and/or pitch data may be used to verify the reliability of other sensor data used to navigate through the environment.

A work vehicle includes: a body; a traveling apparatus capable of a turning travel; a speed detector capable of detecting a vehicle speed; a steering tool manually operable to steer the traveling apparatus; a notification apparatus; and a controller. The controller is configured or programmed to control the traveling apparatus in response to a manual operation, with use of a travel control module; determine, based on a relationship between the vehicle speed and a steering angle of the steering tool, whether at least one of the vehicle speed and the steering angle needs to be reduced, with use of a determination module; and control the notification apparatus to give a notification of the determination by the determination module, with use of a notification module.

A system for ground plane cancellation, comprising an image data system configured to generate image data and associated coordinate data for pixels contained in the image data. A ground plane calculation system configured to generate ground plane coordinate data. A ground plane correction system configured to subtract pixels associated with the ground plane coordinate data from the image data.

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.

The present disclosure relates to a system and a method for avoiding a collision for a vehicle. The system includes an operation determining device for determining whether the vehicle has entered a parking lot based on location information and inclination information of the vehicle, and a controller that generates a virtual map about an obstacle on a curved road in the parking lot based on image information and distance information when it is determined that the vehicle has entered the parking lot, generates an expected movement trajectory of the vehicle based on steering information and speed information of the vehicle, and predicts the collision of the obstacle in the virtual map and the vehicle depending on the expected movement trajectory based on the virtual map and the expected movement trajectory.

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.