The present invention consists in determining at least one dangerous driving indicator (IND) by means of a physical model (MOD) based on the dynamics of the vehicle. According to the invention, the dynamic model (MOD) of the vehicle makes it possible to determine a slip parameter (, SR) of the vehicle, which is used to deduce a representative dangerous driving indicator (IND).

A method to control a road vehicle with steering rear wheels when driving along a curve. The control method comprises the steps of: determining an actual attitude angle of the road vehicle; determining a desired attitude angle; and changing the steering angle of the rear wheels based on the difference between the actual attitude angle and the desired attitude angle.

A running control device changes the distribution of engine braking or regenerative braking and the distribution of friction braking in the entire requested braking force according to an operation amount of an operation element, according to whether the behavior of a vehicle during running is in a stable state or an unstable state, or becomes an unstable state in the near future with high probability.

A lane departure prevention system includes a controller configured to control a braking force of vehicle wheels such that a lane departure prevention yaw moment is applied to a vehicle. The controller determines whether there is a likelihood that the vehicle enters a spinning state based on at least one of a difference between an actual yaw rate and a normative yaw rate of the vehicle calculated based on a steering angle, a vehicle speed, and the lane departure prevention yaw moment, and a degree of braking slip of a turning inside wheel when the lane departure prevention yaw moment is a yaw moment for preventing departure of the vehicle from a lane to a turning outside, and applies a spin prevention yaw moment to the vehicle when it is determined that there is a likelihood that the vehicle will enter the spinning state.

The present disclosure relates to an apparatus for controlling the motion of a vehicle to improve riding comfort, and a method thereof. According to an embodiment of the present disclosure, a processor may determine a boarding location for a user and may determine a vehicle control signal in consideration of riding comfort according to acceleration or jerk based on the boarding location. A controller may control the vehicle depending on the vehicle control signal.

Examples provide a system and method for controlling a vehicle or a fleet of vehicles. The system includes a set of vehicle sensors configured to measure a speed of the vehicle and a motor torque. The system also includes an electronic processor configured to determine a modeled rack force of the vehicle, determine a normal force factor of the vehicle, determine a vehicle speed factor of the vehicle, determine an adjusted rack force based on a product of the modeled rack force, the normal force factor, and the vehicle speed factor, determine a lateral slip angle of the vehicle, and determine a coefficient of friction estimation based on the adjusted rack force and the lateral slip angle. The electronic processor is further configured to control the vehicle based on the coefficient of friction estimation.

System, methods, and other embodiments described herein relate to emulating vehicle dynamics. In one embodiment, a method for emulating vehicle dynamics in a vehicle having a plurality of wheels and equipped with all-wheel steering, includes receiving emulation settings that indicate one or more environment parameters and/or vehicle parameters, detecting driver inputs including at least steering input and throttle input, executing a simulation model that receives the driver inputs and emulation settings, simulates the vehicle operating based on the driver inputs and the emulation settings, and outputs one or more simulated states of the vehicle based on the simulated operation of the vehicle, determining one or more actuation commands for each wheel of the vehicle to cause the vehicle to emulate the one or more simulated states, and executing the one or more actuation commands, wherein the actuation commands include at least wheel angle commands and torque commands.

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.

In one embodiment, a driving scenario is identified for a next movement for an autonomous vehicle, where the driving scenario is represented by a set of one or more predetermined parameters. A first next movement is calculated for the autonomous vehicle using a physical model corresponding to the driving scenario. A sideslip predictive model is applied to the set of predetermined parameters to predict a sideslip of the autonomous vehicle under the driving scenario. A second next movement of the autonomous vehicle is determined based on the first next movement and the predicted sideslip of the autonomous vehicle. The predicted sideslip is utilized to modify the first next movement to compensate the sideslip. Planning and control data is generated for the second next movement and the autonomous vehicle is controlled and driven based on the planning and control data.

A method and a device for estimating coefficients of friction of a wheel of a vehicle with respect to an underlying surface including decomposing a supplied trajectory into individual curve segments, estimating a lateral force and a slip angle for a front axle of the vehicle, assigning respectively estimated lateral forces and slip angles relating to the associated individual curve segments and storing these value pairs in a memory, estimating a tire characteristic curve for each of the curve segments based on the value pairs stored for the respective curve segment in the memory, estimating a coefficient of friction for each curve segment based on the respectively estimated tire characteristic curve, and storing the estimated coefficients of friction relating to the respectively associated curve segments in a coefficient of friction map.