A behavior control device for a vehicle includes: a first actuator configured to apply a vertical control force to a left wheel on a first axle, the first axle being a front axle or a rear axle of the vehicle; a second actuator configured to operate independently of the first actuator and to apply a vertical control force to a right wheel on the first axle; and a controller. The controller is configured to calculate a required value of a behavior parameter representing a behavior of the vehicle, convert the required value of the behavior parameter to a first required force for the first actuator and a second required force for the second actuator, and control the first actuator such that the vertical control force applied to the left wheel on the first axle becomes the first required force.

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.

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 estimated steering data. The estimated steering data may be directly used to navigate through an environment, such as by the vehicle relying on the estimated steering data when planning, tracking, or executing a driving maneuver. Also, the estimated steering data may be used to verify the reliability of other steering sensor data used to navigate through the environment.

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.

A method and system are disclosed for estimating a relative motion of vehicle body portions with respect to each other along a road segment having a length (L); the method and system allow to estimate road unevenness induced vehicle body motions and are based on the estimation of the deformation, over multiple tire rotations, of at least two tires of a vehicle.

A method and system are disclosed for estimating a relative motion of vehicle body portions with respect to each other along a road segment having a length (L); the method and system allow to estimate road unevenness induced vehicle body motions and are based on the estimation of the deformation, over multiple tire rotations, of at least two tires of a vehicle.

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.

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.

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.

A controller and a method for controlling motion of a vehicle is provided. The method comprises acquiring motion information including a current state of the vehicle and a desired state of the vehicle, determining a combination of a steering angle of the wheels and motor forces for moving the vehicle from the current state into the desired state by using a first model of the motion of the vehicle and a second model of the motion of the chassis of the vehicle, determining a cost function of the motion of the vehicle, optimizing the cost function of the motion of the vehicle to compute a command signal for controlling the steering wheel and the plurality of electric motors, and controlling the steering angle of the wheels and the motor forces based on the command signal.