Segment-based driver analysis and individualized driver assistance
Technologies and techniques for controlling a vehicle, in which geometric test data and driving dynamics test data are obtained on a test track. The test data are divided into clusters, and cluster-specific drive-dynamic data is defined for each cluster. Subsequently, the route to be driven is clustered and the vehicle is controlled in a route section in accordance with the specified drive-dynamic data of the respective cluster. Under such configurations, it may be ensured that the driver is supported as individually as possible when driving on unknown routes.
1 . A method for controlling a vehicle on a route to be driven, comprising:
obtaining driving dynamic test data from a test track;
obtaining geometric test data of the test track;
dividing a test data space formed by the geometric test data into clusters;
defining driving dynamic data for each cluster based on the driving dynamic test data that can be assigned to a respective cluster;
assigning sections of a route to be traveled to the clusters, based on a geometric data of the route to be traveled, and
controlling at least one vehicle component of the vehicle in one of the sections according to the defined driving dynamic data of the respective cluster.
2 . The method of claim 1 , wherein the geometric test data include position data, curvature, road width, a combination of road segment types, and/or a number of lanes of the test track.
3 . The method of claim 1 , further comprising obtaining additional situation data regarding weather, time, traffic density, and/or surroundings, wherein the situation data are incorporated into the test data space and the clustering process.
4 . The method of claim 3 , further comprising obtaining current situation data for traveling on the route to be traveled, wherein the current situation data is used for the assigning sections of a route to be traveled to the clusters.
5 . The method of claim 4 , further comprising individually weighting the geometric data of the route to be traveled and/or the current situation data when assigning route segments of the route to be traveled to the clusters.
6 . The method of claim 1 , further comprising obtaining driving dynamic data for multiple driver modes, wherein the defining of the driving dynamic data is based on the multiple driver modes for the route to be traveled.
7 . The method of claim 6 , further comprising receiving a selection of one of the multiple driver modes, wherein the assigning sections of a route to be traveled to the clusters is done depending on the selected driving mode.
8 . The method of claim 1 , wherein the driving dynamic data include a lane guidance, a safety distance, an acceleration, a speed, and/or a jerk.
9 . The method of claim 1 , wherein the assigning of route segments of the route to be traveled to the clusters is based on machine learning.
10 . The method of claim 1 , wherein the test data space comprises vehicle data about vehicle characteristics, and the controlling of the at least one vehicle component is specific to the vehicle characteristics.
11 . A driver assistance system for controlling a vehicle on a route to be traveled, comprising:
a memory for storing sensor-based geometric test data of a test track and sensor-based driving dynamics test data the test track;
one or more hardware processors operably coupled to the memory and configured to execute a clustering algorithm that partitions a test data space formed by the geometric test data into clusters, the hardware processors further configured to determine, for each cluster,
driving dynamics data based on the driving dynamics test data assigned to that cluster, and configured to assign route segments of the route to be traveled to the clusters based on geometric data of the route to be traveled; and
an electronic control unit (ECU) comprising the hardware processors and actuator/driver interfaces, configured to control at least one vehicle component of the vehicle in one of the route segments according to the determined driving dynamic data of a respective cluster.
12 . The driver assistance system of claim 11 , wherein the geometric test data include position data, curvature, road width, a combination of road segment types, and/or a number of lanes of the test track.
13 . The driver assistance system of claim 11 , wherein the hardware processors are configured to obtain additional situation data regarding weather, time, traffic density, and/or surroundings, and to incorporate the situation data into the test data space and the clustering process.
14 . The driver assistance system of claim 13 , wherein the hardware processors are configured to obtain current situation data for traveling on the route to be traveled, wherein the current situation data is used for the assigning sections of a route to be traveled to the clusters.
15 . The driver assistance system of claim 14 , wherein the hardware processors are configured to individually weight the geometric data of the route to be traveled and/or the current situation data when assigning route segments of the route to be traveled to the clusters.
16 . The driver assistance system of claim 11 , wherein the hardware processors are configured to obtain driving dynamic data for multiple driver modes, wherein the determined driving dynamic data is based on the multiple driver modes for the route to be traveled.
17 . The driver assistance system of claim 16 , wherein the hardware processors are configured to receive a selection of one of the multiple driver modes, wherein the data processing device is further configured to assign sections of a route to be traveled to the clusters depending on the selected driving mode.
18 . The driver assistance system of claim 11 , wherein the driving dynamic data comprises a lane guidance, a safety distance, an acceleration, a speed, and/or a jerk.
19 . The driver assistance system of claim 11 , wherein the hardware processors are configured to assign route segments of the route to be traveled to the clusters based on machine learning.
20 . The driver assistance system of claim 11 , wherein the test data space comprises vehicle data about vehicle characteristics, and the controlling of the at least one vehicle component is specific to the vehicle characteristics.