A system and method for slowing a vehicle. Road conditions around the vehicle are monitored, and determined if those road conditions are hazardous. An engine control unit is informed of the hazardous road conditions and alters the operation of the engine control unit in response to the hazardous road conditions. When an operator of the vehicle desires to slow the vehicle down, an indication is received indicating the intent to slow the vehicle down. The vehicle is then slowed based upon the altered operation of the engine control unit by applying a vacuum to increase a manifold vacuum of the engine.

An automatic cruise control system for controlling at least a powertrain of a vehicle, the cruise control system being configured to automatically control a vehicle speed to a target speed determined based on a set speed and on information relating to a road topography along an expected travelling route of the vehicle. The automatic cruise control system is configured to: while automatically controlling the vehicle speed to the target speed, receive an indication that a slippery road condition applies or is expected to apply, in response to receiving said indication, activate a predefined slippery road condition driving mode in which predetermined restrictions apply, said restrictions relating to at least one of the vehicle speed, an allowable vehicle acceleration, and a gear selection of the powertrain, control at least the powertrain in accordance with the slippery road condition driving mode.

A traction control method and a traction control apparatus for a vehicle are provided. The traction control method includes: estimating driving torque for each wheel and a difference between left and right wheel rotation speeds; determining a situation, in which the difference between the left and right wheel rotation speeds exceeds a first set value, to be a split wheel spin situation; estimating a maximum coefficient of friction between a spinning wheel and a road surface in the split wheel spin situation and estimating a maximum driving torque, at which the road surface is acceptable, by the maximum coefficient of friction; and obtaining a difference between driving torque of the spinning wheel and the maximum driving torque to calculate a road surface limitation excess driving torque and determining entry into traction control when the road surface limitation excess driving torque exceeds a second set value.

A rover or semi-autonomous or autonomous vehicle may use an image classifier to determine a terrain class of regions of an image of the terrain ahead of the rover or vehicle. The regions of the images are used to estimate the slope of the terrain for the different regions. The terrain class and slope are used to predict an amount of slip the rover will experience when traversing the terrain of the different regions. A heuristic mapping for the terrain class may be applied to the predicted slip amount to determine a hazard level for the rover or vehicle traversing the terrain.

A hybrid all-wheel-drive vehicle includes an engine, first and second motor generators, a first clutch between the second motor generator and a front wheel, a second clutch between the second motor generator and a rear wheel, and a control unit that controls, based on a vehicle traveling state, the engine, the motor generators, and the clutches. The first motor generator is coupled to the engine and the front wheel in a manner capable of transmitting torque. During regeneration, the control unit engages the first clutch and disengages the second clutch. When the all-wheel-drive vehicle shifts from motor traveling to hybrid traveling, the control unit restarts the engine by operating the first motor generator and regulates engagement forces of the clutches and output torque of the second motor generator to compensate driving torque of the front wheel by the second motor generator while maintaining driving torque of the rear wheel.

A system and method for estimating road conditions ahead of a vehicle, including: a LIDAR sensor operable for generating a LIDAR point cloud; a processor executing a road condition estimation algorithm stored in a memory, the road condition estimation algorithm performing the steps including: detecting a ground plane or drivable surface in the LIDAR point cloud; superimposing an M×N matrix on at least a portion of the LIDAR point cloud; for each patch of the LIDAR point cloud defined by the M×N matrix, statistically evaluating a relative position, a feature elevation, and a scaled reflectance index; and, from the statistically evaluated relative position, feature elevation, and scaled reflectance index, determining a slipperiness probability for each patch of the LIDAR point cloud; and a vehicle control system operable for, based on the determined slipperiness probability for each patch of the LIDAR point cloud, affecting an operation of the vehicle.

A vehicle control system having a subsystem controller for initiating control of a first group of at least one vehicle subsystem in a selected one of a plurality of subsystem control modes each corresponding to one or more different driving conditions; and an estimator module for evaluating at least one driving condition indicator to determine the extent to which each of the subsystem control modes is appropriate and for providing an output indicative of the subsystem control mode that is most appropriate. The estimator module is configured to increase the probability to which the at least one off-road driving mode is determined appropriate in dependence on at least one terrain indicator. In an automatic response mode the subsystem controller selects the most appropriate one of the subsystem control modes for each subsystem of the first group in dependence on the output.

Embodiments of the present invention provide a vehicle control system comprising a speed control system, the speed control system being configured automatically to attempt to cause a vehicle to operate in accordance with a target speed value by causing a first vehicle speed value determined according to a first predetermined method to become or be maintained substantially equal to the predetermined target speed value at least in part by causing application of positive drive torque to one or more wheels by means of a powertrain, wherein the speed control system is configured to impose a constraint on the amount of driving torque that may be demanded of the powertrain in dependence on the target speed value and a second vehicle speed value determined according to a second predetermined method, said a second predetermined method being based on the mean speed of the driven wheels of the vehicle.

The invention relates to a method for jointly controlling asynchronous machines (2; 3) of a motor vehicle (1) having a first asynchronous machine (2) and a second asynchronous machine (3) for driving the motor vehicle (1); an inverter (4), which is designed to supply the first asynchronous machine (2) and the second asynchronous machine (3) with a common stator voltage (5) at a common stator frequency (6). The method comprises the steps of determining a specified setpoint drive torque (11) of the motor vehicle (1) for a current driving situation of the motor vehicle (1); sensing a first rotational speed (7a) of the first asynchronous machine (2) and a second rotational speed (7b) of the second asynchronous machine (3); determining a common operating strategy of the first asynchronous machine (2) and of the second asynchronous machine (3) according to the specified setpoint torque (11) while taking into account the sensed rotational speeds (7a; 7b); and controlling the stator voltage (5) and the stator frequency (6) in order to set the drive torques (9a; 9b) of the asynchronous machines (2; 3) according to the operating strategy.

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.