Systems and methods are provided for predicting a vehicle's motion. It is determined that speed of the vehicle is below a threshold speed. A derated tire cornering stiffness value that is less than a nominal cornering stiffness value is obtained. The vehicle's motion is predicted based on a dynamic model using the derated tire corning stiffness value.

An apparatus for controlling a vehicle includes a vehicle additional yaw moment calculator that calculates, based on a yaw rate of a vehicle, a vehicle additional yaw moment to be applied to the vehicle independently of a steering system, a slipping condition determiner that makes a determination as to a slipping condition of the vehicle, and an adjustment gain calculator that calculates an adjustment gain to adjust the vehicle additional yaw moment so as to reduce the vehicle additional yaw moment additional yaw moment when the vehicle is determined to be in the slipping condition, and increases the adjustment gain in accordance with a degree of a slip of the vehicle when the vehicle is determined to recover from the slipping condition.

The present disclosure discloses an electric vehicle and an active safety control system and method thereof. The system includes: a wheel speed detection module configured to detect a wheel speed to generate a wheel speed signal; a steering wheel rotation angle sensor and a yaw rate sensor module, configured to detect state information of the electric vehicle; a motor controller; and an active safety controller configured to receive the wheel speed signal and state information, obtain state information of a battery pack and state information of four motors, obtain a first side slip signal or a second side slip signal according to the wheel speed signal, the state information, the battery pack and the four motors, and according to the first side slip signal or the second side slip signal, control four hydraulic brakes of the electric vehicle and control the four motors by using the motor controller.

Methods and systems are provided for an improved system and method for validating vehicle lateral velocity estimation. The provided system and method employ an efficient validation algorithm to detect lateral velocity estimation faults. The method and system are robust to road uncertainties and do not require redundant estimations or measurements. The provided system and method offer a technological solution for real time validation of lateral velocity estimation using already existing vehicle sensors, and are independent of (i) road condition information, (ii) wheel torque information, (iii) tire model information, and (iv) tire wear information.

A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

A vehicle and method for controlling the same may include a speed detector configured to detect driving speed of the vehicle, a detection sensor configured to detect a target vehicle around the vehicle and obtain information about at least one of position and speed of the target vehicle, and a controller configured to determine a steering-based avoidance path for the vehicle to avoid the target vehicle by being steered, determine a maximum lateral acceleration of the vehicle for the vehicle to avoid the target vehicle in the steering-based avoidance path, and send a control signal for steering-based avoidance of the vehicle to avoid a collision with the target vehicle based on the determined maximum lateral acceleration.

A method for controlling in real time the path of a motor vehicle travelling in a traffic lane includes detecting a corner in the traffic lane, then, when the vehicle enters the corner, determining first and second quantities for a plurality of successive sampling increments, based on state variables characteristic of the movement of the vehicle, determining a first stored value dependent on the first quantity determined in the current sampling increment and one of the preceding sampling increments, determining a second stored value dependent on the second quantity determined in the current sampling increment and one of the preceding sampling increments, saving the first and second stored values determined for each sampling increment, then, when the vehicle exits the corner determining a value of the understeer gradient depending on the saved first and second stored values, and determining a command for the vehicle based on the understeer gradient.

A vehicle speed estimating apparatus includes a vehicle information receiver configured to receive travel information of a vehicle including a wheel speed, a motor torque, and a longitudinal acceleration of the vehicle, a wheel slip determiner configured to determine whether wheel slip occurs, a longitudinal acceleration calibrator configured to calibrate the longitudinal acceleration received from the vehicle information receiver, and a vehicle speed estimator configured for estimating, depending on a determination result of the wheel slip determiner, a vehicle speed using the wheel speed or the calibrated longitudinal acceleration.

System, methods, and other embodiments described herein relate to tire models based on neural-Exp Tanh parameterization. In one embodiment, a method includes operating a vehicle with a control framework incorporating an Exp Tanh function; calculating prior slip data based on measurements obtained by the control framework; selecting a confidence parameter; using a first predictive model to determine Exp Tanh parameters based upon the prior slip data, the measurements, and the confidence parameter; and inputting a slip parameter and the Exp Tanh parameters into the Exp Tanh function to estimate a tire force.

A system for mitigating an effect of a collision between a vehicle and an obstacle can include a processor and a memory. The memory can store a seat occupancy determination module and a set of modules including a candidate response determination module, a candidate response evaluation module, and a controller module. The seat occupancy determination module can determine a state of a seat with respect to being occupied by a living being, the seat being on a first side opposite of a second side at which an operator is located. The set of modules can cause, in response to the state being: (1) occupied, a first set of operations to be implemented and (2) unoccupied, a second set of operations to be implemented. Each of the first set and the second set can be different from a current trajectory of the vehicle and can mitigate the effect of the collision.