Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. A first vehicle model may be used to determine trajectories for execution by a vehicle equipped with four-wheel steering. A second vehicle model may be used to control the vehicle relative to the determined trajectories. For instance, the second vehicle model can determine leading wheels steering angles for steering leading wheels of the vehicle and trailing wheels steering angles for steering trailing wheels of the vehicle, independently of the leading wheels.

In various examples, systems and methods are disclosed for generating and/or analyzing candidate paths for a multi-body vehicle—e.g., a tractor trailer truck—based on obstacle avoidance considerations and using an uncertainty representation for the vehicle. The uncertainty representation may correspond to a trailer portion of the multi-body vehicle to account for the variations in rotation of the trailer with respect to the tractor. As such, the uncertainty representation may be indicative of a probability that the trailer of the vehicle occupies locations and/or points in world space. This probability—combined with the probability of locations of actors in the environment—may be used to generate candidate paths that satisfy various constraints—e.g., a minimum stochastic distance—between the vehicle and the actor.

A method autonomously controls a mobility of an automotive apparatus, which mobility is such as to have an influence on the path of the apparatus. The method includes steps of: acquiring parameters relative to the path of the apparatus, and of computing a new control setpoint for the mobility of the apparatus depending on said parameters, this new control setpoint being determined by means of a controller that respects a model that limits the variation in the control setpoint.

An automotive electronic dynamics control system for a motor-vehicle equipped with and automotive automated driving system designed to cause the motor-vehicle to perform low-speed manoeuvres in automated driving.

The automotive automated driving system comprises an automotive sensory system designed to detect motor-vehicle-related quantities, and automotive actuators comprising an Electric Power Steering, a Braking System, and a Powertrain.

The electronic dynamics control system is designed to implement a Driving Path Planner designed to: receive data representative of static obstacles in the surroundings of the motor-vehicle and representing static space constraints to the motion of the motor-vehicle, and compute, based on the received data, a planned driving path for the motor-vehicle during a low-speed manoeuvre performed in automated driving.

The electronic dynamics control system is further designed to implement a Model Predictive Control (MPC)-based Trajectory Planner and Controller designed to: receive from the Driving Path Planner data representative of the planned driving path and from the automotive sensory system data representative of positions and orientations of the motor-vehicle and of dynamic obstacles in the surroundings of the motor-vehicle and representing dynamic space constraints to the motion of the motor-vehicle, and compute, based on the received data, a planned lateral trajectory and a planned longitudinal trajectory for the motor-vehicle during the low-speed manoeuvre performed in automated driving.

The electronic dynamics control system is further designed to implement a Motion Controller designed to: receive from the Trajectory Planner and Controller data representative of the planned lateral and longitudinal trajectories, and compute commands for the Electric Power Steering based on the planned lateral trajectory, and for the Braking System and the Powertrain based on the planned longitudinal trajectory.

The Driving Path Planner is designed to compute the planned driving path as a planned driving corridor within which the motor-vehicle may be driven and made up of a series of driving path segments each with a length and an orientation referenced in an inertial reference frame.

The MPC-based Trajectory Planner and Controller comprises: an MPC-based Lateral Trajectory Planner and Controller designed to compute the planned lateral trajectory as a series of steering requests referenced in a motor-vehicle reference frame; and an MPC-based Longitudinal Trajectory Planner and Controller designed to compute the planned longitudinal trajectory as a series of longitudinal acceleration requests.

The Late

A method for determining a slip angle λ.sub.r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control is provided. The slip angle λ.sub.r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle λ.sub.r1, λ.sub.r2 or λ.sub.r3 is determined by one of the three steps representing the slip angle λ.sub.r or the slip angle λ.sub.r is determined from at least two of the slip angles λ.sub.r1, λ.sub.r2 and λ.sub.r3.

A device and a method for improving a turning motion of a vehicle may improve turning stability by cooperative control of an electric motor and the electronic controlled suspension (ECS) and improve behavior stability by optimizing a pitch/roll behavior by allowing realization of a target yaw moment required to improve turning characteristic of the vehicle to be reinforced by not only a yaw moment directly generated by a braking torque or a driving torque of the electric motor, but also a yaw moment indirectly generated by a load movement caused by controlling a damping force of the electronic controlled suspension (ECS).

Systems and methods are provided for uncertainty based contingency model predictive control of a vehicle in uncertain road conditions. Applications may be found for collision imminent steering, on-road autonomous vehicles and real time decision-making influenced by an unknown environment. An uncertainty road coefficient of friction may be estimated using an Unscented Kalman filter, and the controller may be updated based upon the estimated uncertainties to provide for collision avoidance in unknown conditions.

A method is for determining a side slip angle during the cornering of a vehicle. The following variables are recorded and interlinked via a mathematical vehicle model with assumptions of the linear single-track model: a predetermined or measured position of the center of gravity between a front and rear axle, the current vehicle velocity, a current vehicle cornering motion variable, the current steering angle on the front axle. To simplify the determination of the side slip angle, it is determined under the assumption that the difference between the side slip angle and the Ackermann side slip angle is proportional to the difference between the Ackermann angle and the steering angle. The actual side slip angle is deduced from the relationship of the measured steering angle and the Ackermann angle based on the proportionality relationship of the Ackermann side slip angle theoretically present when driving through the same curve without slip.

A wind data estimating apparatus includes one or more processors configured to collect vehicle information including a first acceleration, an amount of driving operation performed by a driver of a vehicle, and position information, which are obtained by sensors installed in the vehicle; classify the collected vehicle information by an area of a plurality of areas according to the position information; and estimate a wind velocity and a wind direction for the area and for a time range when the vehicle information is obtained, on the basis of an acceleration obtained from subtracting a second acceleration caused by the amount of driving operation from the first acceleration included in the vehicle information classified by the area.

A method for setting an anticipator module with which a control device controls the trajectory of a motor vehicle is equipped includes detecting whether the anticipator module is unsuitable during a turn by taking account of a lateral deviation with respect to an ideal trajectory and/or a contribution of a feedback module of the control device, determining primary parameters, calculating a secondary parameter by an optimization-based calculation method taking account of the determined primary parameters, and updating a bicycle model of the vehicle by taking account of the calculated secondary parameter.