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.

A moving track prediction method includes obtaining an initial state, of a moving target that includes an initial location and an initial motion state, generating one or more destination states of the moving target based on the initial state of the moving target and preset path information, and predicting a moving track of the moving target based on the initial state of the moving target and the one or more destination states to obtain one or more predicted moving tracks.

A method for estimating a position of a trailer relative to a truck of a road train includes providing or obtaining a kinematic model of the road train, providing or obtaining a length estimation of the trailer, and measuring an articulation angle between the truck and the trailer. The method further includes determining a position of the trailer relative to the truck using the kinematic model, the length estimation, and the measured articulation angle.

A control apparatus for controlling a vehicle includes a driving motor configured to drive the vehicle by outputting motor torque based on a supply voltage from a battery, and an engine configured to drive the vehicle by outputting engine torque. The control apparatus may acquire driving mode data which is calculated based on traffic information from the current position to the destination of the vehicle and dimension information of the vehicle, and control the vehicle to drive to the destination according to a driving mode which is determined by applying a travelling condition of the vehicle to the acquired driving mode data, where the power distribution ratio of the motor torque to the engine torque is reflected in the driving mode data.

Techniques for controlling a vehicle based on a collision avoidance algorithm are discussed herein. The vehicle receives sensor data and can determine that the sensor data represents an object in an environment through which the vehicle is travelling. A computing device associated with the vehicle determines a collision probability between the vehicle and the object at predicted locations of the vehicle and object at a first time. Updated locations of the vehicle and object can be determined, and a second collision probability can be determined. The vehicle is controlled based at least in part on the collision probabilities.

A method includes providing a simulation environment that simulates a vehicle. The method includes providing the vehicular lane centering algorithm to the simulation environment, generating a base scenario for the vehicular lane centering algorithm for use by the simulated vehicle, and extracting, from the simulation environment, traffic lane information. The method also includes measuring performance of the vehicular lane centering algorithm during the base scenario using the extracted traffic lane information and generating a plurality of modified scenarios derived from the base scenario. Each modified scenario of the plurality of modified scenarios adjusts at least one parameter of the base scenario. The method also includes measuring performance of the vehicular lane centering algorithm during the plurality of modified scenarios using the extracted traffic lane information.

Example embodiments relate to methods and systems for automatic problematic maneuver detection and adapted motion planning. A computing device may obtain a route for navigation by a vehicle and a set of vehicle parameters corresponding to the vehicle. Each vehicle parameter can represent a physical attribute of the vehicle. The computing device may generate a virtual vehicle that represents the vehicle based on the set of vehicle parameters and perform a simulation that involves the virtual vehicle navigating the route. Based on the results of the simulation, the computing device may provide the original route or a modified route to the vehicle for the vehicle to subsequently navigate to its destination. In some cases, the simulation may further factor additional conditions, such as potential weather and traffic conditions that are likely to occur during the time when the vehicle plans on navigating the route.

An illustrative example method is for estimating a friction characteristic of a surface beneath a vehicle that has a plurality of wheels contacting the surface. The method includes determining a wheel speed of at least one of the wheels, determining a velocity of the at least one of the wheels separately from determining the wheel speed, determining a wheel slip of the at least one of the wheels based on the determined wheel speed and the determined velocity, and determining the friction characteristic based on the determined wheel slip. Determining the velocity separately from the wheel speed is accomplished using at least one detector that provides an output corresponding to a range rate, such as a RADAR or LIDAR detector.

In a vehicle control system, a risk frequency calculation unit counts the number of times that a risk level determination unit determines that a risk level is equal to or higher than a predetermined risk level within a predetermined period. Then, a warning frequency setting unit sets a frequency of warning in accordance with the number of times, counted by the risk frequency calculation unit, that the risk level of the driving operation by the driver is determined to be equal to or higher than the predetermined risk level within the predetermined period and activates a warning device.

A number of illustrative variations may include a system and method of controlling vehicle slowing while implementing brake-to-steer functionality that may include providing a feed-forward gain on vehicle propulsion torque to achieve or maintain target longitudinal acceleration and replicate the behavior of a vehicle not using brake-to-steer. The system may manipulate propulsion of the vehicle to manage longitudinal acceleration disturbance and speed disturbance during brake-to-steer.