A traction control system for a motor vehicle includes a controller configured to initiate a traction control intervention at one or more vehicle wheels. The controller is configured to inhibit the traction control intervention in dependence on a reduced wheel load condition in said one or more wheels. The reduced wheel load condition is identified based on at least one of a signal indicative of vehicle pitch and a signal indicative of vehicle heave.

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 real-time assessment method of driving risk based on equivalent force includes: S1, collecting traffic environment information and various types of traffic environment use object information in a road environment in an area to be assessed; S2, inputting, into an electronic control unit of a vehicle, the traffic environment use object information and the environment information acquired in S1, wherein a road risk assessment model based on the equivalent force distribution is preset in the electronic control unit; S3, using the road risk assessment model, so as to acquire road traffic risk E of the vehicle i and equivalent force distribution F.sub.ij between the vehicle i and the object j in different traffic environments, wherein the object j represents any traffic element other than vehicle i in various traffic environment use object information. A real-time assessment device of driving risk based on equivalent force is further provided.

An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Method for the estimation of at least a variable (β; ν.sub.x, ν.sub.y; ψ, μ) affecting a vehicle dynamics (10), including measuring dynamic variables (MQ) of the vehicle (10) during its motion, calculating in real time an estimate (Formula (I)) of said variable (β; ν.sub.x, ν.sub.y; ψ, μ), on the basis of said measured dynamic variables (MQ), The method includes: calculating (230) said estimate of said at least a variable (β; ν.sub.x, ν.sub.y; ψ, μ) by an estimation procedure (DVS.sub.β; DVS.sub.βν; DVS.sub.βνμ) comprising taking in account a set of dynamic variables (MQ) measured during the motion of the vehicle (10) over respective time intervals (n.sub.y, n.sub.w, n.sub.ψ, n.sub.x, n.sub.α) and applying on said set of measured dynamic variables (MQ) at least an optimal nonlinear regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ.sub.ψ2) calculated with respect to said variable (β; ν.sub.x, ν.sub.y; ψ, μ) to estimate to obtain said estimate of said variable (β; ν.sub.x, ν.sub.y; ψ, μ), said optimal non linear regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ*.sub.ψ2) being obtained by an optimal calculation procedure (220) including: on the basis of an acquired set of reference data (D.sub.d) and of said set of dynamic variables (MQ) measured during the motion of the vehicle (10), finding, for a desired accuracy level (ε), a regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ*.sub.ψ2) giving an estimation error lower or equal than said desired accuracy level (ε) in a given set of operative conditions (OC), said acquired set of reference data (D.sub.d) being obtained by acquiring (210) in said given set of operative conditions (OC) a set of reference data (D.sub.d) of variables including variables corresponding to said measured dynamic variables (MQ) of the vehicle (10) and a lateral (v.sub.y) and a longitudinal velocity (v.sub.x) of the vehicle (10).

A system including a first drive axle, a second drive axle, a first sensor, a second sensor, and a controller. The first sensor is configured to measure a first speed of the first drive axle. The second sensor is configured to measure a second speed of the second drive axle. The controller is in communication with the first and second sensors. The controller configured to determine an actual axle speed difference value based on the measured first speed and the measured second speed, determine an expected axle speed difference value based on a vehicle speed and a vehicle torque, compare the actual axle speed difference value and the expected axle speed difference value to obtain an error value, and generate an output signal in response to the error value being above a predetermined threshold value.

A method for operating a motor vehicle having a hybrid drive, and to a motor vehicle configured for carrying out the method. In a hybrid operating mode of the motor vehicle, a proportion of a purely electric driving operation is maximized by controlling the automatic transmission so a resulting rotational speed is not lower than a preset minimum value.

A driving force control apparatus for controlling a driving force to be transmitted to a wheel includes a processor. The processor is configured to set, when the wheel is idled, a control amount of the driving force to be transmitted to the wheel based on a vehicle acceleration.

A system for controlling movement of a vehicle includes a user input device and computing system. The user input device dynamically controls a settings or balance of driving dynamics in a vehicle, and the user input device is configured to receive a manual input from a user. The computing system controls the settings of the vehicle driving dynamics and/or balance of the vehicle, the computing system is in data communication with the user input device and configured to change the driving dynamics balance proportionately to the manual input upon receiving an input command based on the manual input from the user input device.

A method and sensor system are disclosed for automatically determining object sensor position and alignment on a host vehicle. A radar sensor detects objects surrounding the host vehicle in normal operation. Static objects are identified as those objects with ground speed approximately equal to zero. Vehicle dynamics sensors provide vehicle longitudinal and lateral velocity and yaw rate data. Measurement data for the static objects—including azimuth angle, range and range rate relative to the sensor—along with the vehicle dynamics data, are used in a recursive geometric calculation which converges on actual values of the radar sensor's two-dimensional position and azimuth alignment angle on the host vehicle.