A control system includes one or more processing circuits comprising one or more memory devices coupled to one or more processors. The one or more memory devices are configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to acquire speed data regarding current speeds of tractive elements of the vehicle from tractive element speed sensors of the vehicle, determine speed references for the tractive elements to perform autonomous driving operations where the speed references indicate speeds at which each of the tractive elements should rotate to accommodate the autonomous driving operations, and control at least one of a driveline or a brake system of the vehicle to selectively alter the current speeds of the tractive elements of the vehicle based on the current speeds and the speed references to accommodate the autonomous driving operations.

Provided is a conveying vehicle that ensures efficiently travelling while suppressing vehicle slip. A dump truck 100 includes a vehicle body 101 provided with wheels 103 and a vehicle control device 300 and travels on a travel route. The vehicle control device 300 calculates and stores slip limit values at a plurality of positions on the travel route, reads out the slip limit values to calculate at least one of a maximum acceleration and a maximum deceleration of the dump truck 100 at which the wheels 103 is capable of maintaining a grip state against a road surface, and sets a target travel speed at a travel position between the dump truck 100 and a target position according to a target speed at the target position and at least one of the maximum acceleration and the maximum deceleration.

A vehicle control device includes a processor. The processor is configured to: output a torque command value related to a rotation speed of a wheel of a vehicle; specify an estimated value which is a value obtained by estimating the rotation speed of the wheel based on the torque command value; and determine a parameter based on an error between the estimated value and a measured value which is a value obtained by measuring the rotation speed of the wheel. The torque command value is determined by a feedforward control using a target value which is a value as a target of the rotation speed of the wheel and the parameter.

Systems and methods are provided herein for controlling the speed on each wheel of a vehicle, possibly operating a vehicle in a speed control mode. In response to receiving input to engage speed control mode and receiving an accelerator pedal input, the system determines a target wheel speed based on the accelerator pedal input, monitors wheel speed of each of a plurality of wheels and determines, for each monitored wheel, a difference based on the monitored wheel speed and the target wheel speed. A torque is provided to each of the plurality of wheels based on the respective difference to achieve the target wheel speed.

Embodiments of the present disclosure relate to the technical field of autonomous driving, and in particular to methods for updating an autonomous driving system, autonomous driving systems, and on-board apparatuses. In the embodiments of the present disclosure, the autonomous driving system, in a manual driving mode, senses the surrounding environment of a vehicle, performs vehicle positioning, and plans a path for autonomous driving for the vehicle. However, the autonomous driving system does not issue an instruction to control the driving of the vehicle. Instead, it compares the path with a path along which a driver drives the vehicle in the manual driving mode to update a planning and control algorithm of the autonomous driving system. As such, the updated autonomous driving system better caters to the driving habits of the driver and improves the driving experience for the driver without compromising the reliability of planning and decision-making of autonomous driving.

A tire longitudinal stiffness estimation system includes an electronic communication system disposed on a vehicle. A sensor is disposed on the vehicle in communication with the electronic communication system, and a processor is accessible through the electronic communication system. The sensor measures parameters associated with the vehicle and communicates data for the parameters to the processor. A mu slip curve generator receives the parameters to generate a mu slip curve in real time from the data. An extraction module extracts raw data from a linear portion of the mu slip curve. A denoising module de-noises the raw data from the mu slip curve by determining a vector for the raw data, an orientation of the vector, and a heading of the vector. The denoising module generates de-noised data, and a stiffness calculator receives the de-noised data and generates a longitudinal stiffness estimate for the tire.

A vehicle comprises an autonomous driving system and a vehicle platform that controls the vehicle in response to a command received from the autonomous driving system. In the vehicle, when a first signal indicates an autonomous mode, the vehicle platform performs a shift change requested through a first command only while the second signal indicates a standstill.

A parking control apparatus, for a vehicle including a plurality of wheels, that includes: a processor that is configured to: individually control the wheels to be braked, released, or driven, and, based on control of the wheels, turn the vehicle about an axis that is perpendicular to ground is disclosed.

A driving consciousness estimation device includes a driving readiness estimation unit configured to estimate a driving readiness relating to a driving consciousness of the driver from a driver's reaction to the travelling environment, a driving task demand estimation unit configured to estimate a driving task demand which is an index required for the driver with respect to the driving readiness from the travelling environment, and an attention awakening unit configured to execute awakening of attention for the driver relating to the driving of the vehicle based on the result of comparison between the driving readiness and the driving task demand.

Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.