A control system for use in a motor vehicle and configured to monitor a current driving situation of the motor vehicle on the basis of surrounding data of the motor vehicle acquired from at least one surrounding sensor arranged on the motor vehicle in a current driving situation is disclosed. The control system is configured to determine information relating to a current driving situation of the motor vehicle on the basis of the provided surrounding data, to determine information relating to a current driving situation of the motor vehicle and to determine a component of a future driving maneuver for the motor vehicle on the basis of the information relating to the current driving situation of the motor vehicle. Furthermore, the control system is configured to determine a multiplicity of model trajectories for the motor vehicle on the basis of the determined component of the future driving maneuver for the motor vehicle and to determine from the multiplicity of model trajectories a trajectory for the motor vehicle which the motor vehicle is to follow in the further course of its travel. The control system is also configured to update the information relating to the current driving situation of the motor vehicle and/or the supplied surrounding data and to adapt the trajectory for the motor vehicle on the basis of a target function and on the basis of the updated supplied surrounding data and/or on the basis of the updated information relating to the current driving situation of the motor vehicle.

A method, a device, and a system for parking a truck accurately in a shore crane area are provided. A vehicle controller transmits a parking request for a truck to be parked. A main controller receives the parking request and acquires real-time point cloud data by scanning one or more lanes crossed by a shore crane using one or more LiDARs. The main controller clusters the real-time point cloud data to obtain a set of point clouds for the truck and applies an Iterative Closest Point algorithm to the set of point clouds and a vehicle point cloud model to obtain a real-time distance from the truck to a target parking space. The vehicle controller controls the truck to stop at the target parking space based on the real-time distance.

A computer includes a processor and a memory storing instructions executable by the processor to receive a series of sample coordinate points of a projected path of travel of a vehicle, generate interpolated coordinate points along the projected path between the sample coordinate points, fit a curve to the sample coordinate points and interpolated coordinate points, and output a curvature of a lane at a reported coordinate point along the projected path based on the curve.

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

The cloud includes a server and a storage device. The storage device includes a road surface information map. When a first sampling distance is equal to or longer than a first distance threshold, the server performs re-sampling to interpolate data in such a manner that sampling positions located at a second sampling distance and unsprung mass member displacements of the respective sampling positions exist so as to produce re-sampled data-for-producing-map. The server stores a sub-sectional unsprung mass displacement in a storage area corresponding to a sub-section of the road surface information map, based on the re-sampled data-for-producing-map.

The present invention relates to a method and device for estimating the road surface friction coefficient of a tire, which estimate the road surface friction coefficient of a tire mounted on a wheel of a vehicle in a state in which the vehicle is normally running at high speed. The method includes: acquiring the state information of a vehicle including at least one of engine state information, transmission state information, and chassis state information from sensors mounted on the vehicle and specifications set for the vehicle; estimating a longitudinal slip ratio, normal force, and longitudinal force for a tire mounted on each wheel of the vehicle by using the acquired state information of the vehicle; and estimating a road surface friction coefficient for the tire by using the estimated longitudinal slip ratio, normal force, and longitudinal force.

A moving object control device includes: a moving object position acquiring unit acquiring moving object position information indicating a position of a moving object; a target position acquiring unit acquiring target position information indicating a target position to which the moving object is caused to travel; and a control generating unit generating a control signal indicating a control content for causing the moving object to travel toward the target position on a basis of model information indicating a model that is trained using a calculation formula for calculating a reward including a term for calculating a reward by evaluating whether or not the moving object is traveling along a reference route by referring to reference route information indicating the reference route, the moving object position information acquired by the moving object position acquiring unit, and the target position information acquired by the target position acquiring unit.

Provided is a method for controlling movement of an autonomous mobile machine. The method includes that: a target path and a current state of the autonomous mobile machine are acquired; at least one preview distance is calculated according to a current speed; at least one preview point each corresponding to a respective one of the at least one preview distance is acquired according to the target path and the at least one preview distance; a lateral deviation from each preview point to a current position is calculated; a direction control angle parameter of a current control period is acquired according to the lateral deviation, the current speed and a preset parameter matching table; and the autonomous mobile machine is controlled to move according to the direction control angle parameter. Also provided are an apparatus for controlling movement of an autonomous mobile machine, a machine and a storage medium.

Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.

The cloud includes a server and a storage device. The storage device includes a road surface information map. When a first sampling distance is equal to or longer than a first distance threshold, the server performs re-sampling to interpolate data in such a manner that sampling positions located at a second sampling distance and unsprung mass member displacements of the respective sampling positions exist so as to produce re-sampled data-for-producing-map. The server stores a sub-sectional unsprung mass displacement in a storage area corresponding to a sub-section of the road surface information map, based on the re-sampled data-for-producing-map.