A method of controlling braking when steering an in-wheel motor vehicle includes monitoring a required tire rotation angle for each steering angle and an actual tire rotation angle when performing cooperative control of an in-wheel motor for reducing a steering load, and generating a vehicle braking force in a case where the actual tire rotation angle exceeds the required tire rotation angle, thereby easily preventing a vehicle-skidding phenomenon.

A method of controlling braking when steering an in-wheel motor vehicle includes monitoring a required tire rotation angle for each steering angle and an actual tire rotation angle when performing cooperative control of an in-wheel motor for reducing a steering load, and generating a vehicle braking force in a case where the actual tire rotation angle exceeds the required tire rotation angle, thereby easily preventing a vehicle-skidding phenomenon.

A determining method for determining a state of a road surface includes: sequentially acquiring a rotational speed of tires mounted on the vehicle, sequentially acquiring a driving force of the vehicle, calculating a slip ratio based on the sequentially acquired rotational speed of the tires, calculating a regression equation and a confidence interval width for a relationship between the slip ratio and the driving force, based on data sets of the slip ratio and the driving force in a predetermined zone, and determining a state of the road surface on which the vehicle travels, based on the confidence interval width calculated for the predetermined zone.

A determining method for determining a state of a road surface includes: sequentially acquiring a rotational speed of tires mounted on the vehicle, sequentially acquiring a driving force of the vehicle, calculating a slip ratio based on the sequentially acquired rotational speed of the tires, calculating a regression equation and a confidence interval width for a relationship between the slip ratio and the driving force, based on data sets of the slip ratio and the driving force in a predetermined zone, and determining a state of the road surface on which the vehicle travels, based on the confidence interval width calculated for the predetermined zone.

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.

A computer implemented method for controlling a vehicle includes obtaining a value of the mass of the vehicle, receiving a plurality of time sequential measured first values of one or more further state parameters, calculating a first plurality of time sequential values of the vehicle mass, including a first calculated mass value, using the plurality of measured first values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a first filter tuning, with the obtained mass value as a start value, receiving a plurality of time sequential measured second values of the one or more of the further state parameters, and calculating a second plurality of time sequential values of the vehicle mass, including a second calculated mass value, using the plurality of measured second values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a second filter tuning, with the first calculated mass value as a start value, wherein the second filter tuning is made less aggressive than the first filter tuning.

A computer implemented method for controlling a vehicle includes obtaining a value of the mass of the vehicle, receiving a plurality of time sequential measured first values of one or more further state parameters, calculating a first plurality of time sequential values of the vehicle mass, including a first calculated mass value, using the plurality of measured first values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a first filter tuning, with the obtained mass value as a start value, receiving a plurality of time sequential measured second values of the one or more of the further state parameters, and calculating a second plurality of time sequential values of the vehicle mass, including a second calculated mass value, using the plurality of measured second values of the one or more further state parameters, the non-linear model, and an extended Kalman filter with a second filter tuning, with the first calculated mass value as a start value, wherein the second filter tuning is made less aggressive than the first filter tuning.

A method for classification of ground conditions in the vicinity of a vehicle using a radar sensor, comprising: receiving reflected portions of a radar signal at a receiver unit of a radar system; calculating information derived from the received portions of the radar signal for discrete spatial regions by the radar system or a control unit connected thereto; assigning the information to data structure units associated with a geographical location and the assignment of the information taking into account movement of the vehicle; collecting pieces of information in the respective data structure units, the pieces of information being obtained from reflected portions of radar signals transmitted at different times; evaluating the information contained in the data structure using a classifier to obtain information regarding the ground condition; assigning ground condition types to the data structure units based on evaluation results obtained by the classifier.

A method for classification of ground conditions in the vicinity of a vehicle using a radar sensor, comprising: receiving reflected portions of a radar signal at a receiver unit of a radar system; calculating information derived from the received portions of the radar signal for discrete spatial regions by the radar system or a control unit connected thereto; assigning the information to data structure units associated with a geographical location and the assignment of the information taking into account movement of the vehicle; collecting pieces of information in the respective data structure units, the pieces of information being obtained from reflected portions of radar signals transmitted at different times; evaluating the information contained in the data structure using a classifier to obtain information regarding the ground condition; assigning ground condition types to the data structure units based on evaluation results obtained by the classifier.

A vehicle management system includes: a map calculation unit that calculates a slip map in which a traveling position of an autonomous vehicle and a statistical value of slip index values on the traveling position are associated; and a vehicle extraction unit that extracts, as a target vehicle, the autonomous vehicle that has measured an outlier of the slip index value on a traveling route compared to the slip map.