A method for processing friction data for vehicle tires on road segments, implemented by a processing system including at least one computer and an interface for remote communication with a plurality of vehicles, the method including: acquiring, from the plurality of vehicles, friction data for tires of the vehicles on a plurality of road segments, each friction datum including at least: a maximum coefficient of friction available to the vehicle on the road segment, and information relating to the road segment; establishing, for each road segment, a distribution of the friction data obtained from the plurality of vehicles for the road segment; and determining a plurality of road types, each road type comprising a set of road segments, from a measurement of similarity between the distributions of friction data obtained for each road segment.

A vehicle control device configured to control switching of drive mode of a vehicle including an internal combustion engine and a motor includes a processor configured to switch, in a case where a road surface of a perimeter of a geofencing zone is a road surface on which there is a high probability that the vehicle slips, in a movement route from an outside of the geofencing zone to an inside of the geofencing zone, the drive mode of the vehicle to drive by the motor in a state in which there is a low probability that the vehicle slips, outside the geofencing zone.

We disclose adaptive active safety systems which utilize a sensing system to detect road conditions and actuate an active safety routine based thereon. One disclosed method includes transmitting, with a multispectral LIDAR system on a vehicle, a multispectral light beam directed at an anticipated region of travel of the vehicle within a road, and analyzing a response, of a photodetector, to a return of the multispectral light beam. The method also includes determining, based on the analyzing of the response, a hazardous surface condition in the anticipated region of travel of the vehicle, and actuating, based on the determination of the hazardous surface condition, an active safety routine. The method can be executed by an ADAS in which the determinations of the hazardous surface conditions on the road in the anticipated region of travel are executed in real time to thereby produce a road aware ADAS.

Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving a first surface value associated with a first road surface area in an upcoming environment of the vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface value based on the first surface value and the second surface value; and in response to the change in surface value being greater than a threshold, adapting at least one of vehicle collision warning messages, vehicle braking control, vehicle steering control, and path planning based on the second surface value.

System, methods, and other embodiments described herein relate to skid recovery for a vehicle. In one embodiment, a method for controlling a vehicle during skid includes obtaining data indicating a skid condition of the vehicle, determining whether the skid condition can be corrected by counter-steering, and executing an intervention when the skid condition cannot be corrected by counter-steering, the intervention including inducing slippage in front wheels of the vehicle to change a direction and/or magnitude of lateral forces at the front wheels.

A non-transitory computer readable storage medium has instructions executed by a processor to obtain the relative speed between a first traffic object and a second traffic object. The separation distance between the first traffic object and the second traffic object is received. The relative speed and the separation distance are combined to form a quantitative measure of hazard encountered by the first traffic object. The obtain, receive and combine operations are repeated to form cumulative measures of hazard associated with the first traffic object. The cumulative measures of hazard are analyzed to derive a first traffic object safety score for the first traffic object.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

The present invention provides a vehicle control apparatus, a vehicle control method, and a vehicle control system capable of optimizing balance between a target tire lateral force and a target tire longitudinal force. A vehicle control apparatus outputs an instruction for achieving an optimal slip ratio corresponding to a minimum value of a sum of a first difference and a second difference to an actuator regarding braking/driving of a vehicle. The first difference is a difference between a tire lateral force and a target tire lateral force with respect to an arbitrary slip ratio in a correlative relationship between a slip ratio and the tire lateral force of a tire of a wheel portion. The second difference is a difference between a tire longitudinal force and a target tire longitudinal force with respect to the arbitrary slip ratio in a correlative relationship between the slip ratio and the tire longitudinal force.

The disclosure relates to the process of ascertaining an input variable of a vehicle actuator using a model-based predictive control. According to one exemplary arrangement, a processor unit is designed to access trajectory information and a state data set, which represents a state of surroundings of a vehicle and/or the state of the vehicle and/or a driving state of the vehicle, by an interface. The processor unit carries out a secondary condition algorithm in order to calculate a secondary condition and an MPC algorithm for a model-based predictive control. By carrying out the secondary condition algorithm, a secondary condition is ascertained for the MPC algorithm on the basis of the trajectory information and on the basis of the state data set. By carrying out the MPC algorithm, an input variable is ascertained for an actuator of the vehicle on the basis of the secondary condition. This is carried out in particular such that in a future predicted trajectory, the vehicle follows the specified trajectory with a specified degree of reliability.

A drive assist apparatus configured to set a drive condition of a vehicle based on a risk map generated by giving a risk potential to a risk object that is present around the vehicle, includes one or more processors and one or more memories connected to the one or more processors to be able to communicate with the one or more processors. The one or more processors is configured to execute a process including: obtaining information on a surrounding environment of the vehicle; obtaining information on an external environmental factor that may cause deviation of a drive track of the vehicle; and expanding a setting range of the risk potential of the risk object which is positioned in a direction of the expected deviation based on the information on the external environmental factor.