Vehicle dynamics prediction with lane/path information using a preview-correction-prediction approach
A method for predicting the dynamics of a vehicle using information about the path on which the vehicle is travelling that has particular application for enhancing active safety performance of the vehicle, to improve driver comfort and to improve vehicle dynamics control. The method includes generating a preview of a path to be followed by the vehicle where the preview of the path is generated based on actual values of a plurality of vehicle parameters. The method further includes obtaining a corrected value of at least one of the plurality of vehicle parameters corresponding to the actual values of each of the plurality of vehicle parameters, wherein the corrected value of the at least one of the vehicle parameters is obtained based on a target path to be followed by the vehicle on the road, and wherein the target path is obtained on the basis of a plurality of road parameters.
1. A recursive method for predicting dynamics of a vehicle moving on a road, the dynamics of the vehicle including a plurality of vehicle parameters provided by sensors on the vehicle, said method comprising:
performing the following operations by a processor on the vehicle;
generating a preview of a path to be followed by the vehicle, where the preview of the path is generated based on actual values of the plurality of vehicle parameters;
obtaining predicted values of at least one of the plurality of vehicle parameters corresponding to the actual values of the at least one of the plurality of vehicle parameters, where the predicted values are obtained based on a target path to be followed by the vehicle on the road, wherein the target path is obtained based on a plurality of road parameters;
obtaining a corrected value of at least one of the plurality of vehicle parameters corresponding to the actual values of each of the vehicle parameters, where the corrected value is obtained based on minimizing the difference between the previewed path and the target path to be followed by the vehicle on the road;
predicting the dynamics of the vehicle based on the corrected values of the at least one of the plurality of vehicle parameters; and
repeating the steps of generating the preview of the path, obtaining the corrected value of the at least one of the plurality of vehicle parameters and predicting the dynamics of the vehicle, wherein the steps are repeated after a pre-defined interval of time, and wherein the actual values of the at least one of the plurality of vehicle parameters is replaced by the predicted values of the at least one of the plurality of vehicle parameters.
2. The method according to claim 1 further comprising dividing a preview distance of the vehicle into a number of sampling segments, wherein the preview distance is a distance for which the steps are repeated, and wherein each of the sampling segments is located at the pre-defined interval of time.
3. The method according to claim 1 wherein generating preview of the path includes estimating the dynamics and kinematics of the vehicle.
4. The method according to claim 3 wherein estimating the dynamics of the vehicle includes estimating a value of the vehicle lateral velocity, and wherein the value of the vehicle lateral velocity is estimated using the actual values of the plurality of vehicle parameters.
5. The method according to claim 3 wherein the estimated kinematics of the vehicle are used to generate the preview of the path to be followed by the vehicle, and wherein the preview is generated by using the value of the vehicle lateral velocity.
6. The method according to claim 1 wherein the plurality of vehicle parameters is at least one of the group comprising vehicle speed, vehicle yaw rate and an input given to a steering wheel of the vehicle.
7. The method according to claim 1 wherein the actual values of the plurality of vehicle parameters are obtained using a sensing module.
8. The method according to claim 1 wherein the plurality of road parameters are obtained from at least one of the group comprising a global positioning system, a camera and a collection of maps.
9. The method according to claim 1 wherein the dynamics of the vehicle are predicted using a Luenberger state observer and a state integration function, and wherein the state integration function uses the predicted values of the at least one of the plurality of vehicle parameters.
10. The method according to claim 1 wherein repeating the steps of generating the preview of the path, obtaining the corrected value of the at least one of the plurality of vehicle parameters and predicting the dynamics of the vehicle after a predetermined time interval include the use of co-ordinate transform techniques.
11. A recursive method for predicting dynamics of a vehicle moving on a road, the dynamics of the vehicle including a plurality of vehicle parameters provided by sensors on the vehicle, the plurality of vehicle parameters including a vehicle speed, a vehicle yaw rate and an input given to a steering wheel of the vehicle, said method comprising:
performing the following operations by a processor on the vehicle;
generating a preview of a path to be followed by the vehicle on the road where the preview of the path is generated based on actual values of the vehicle speed, the vehicle yaw rate and the input given to a steering wheel of the vehicle;
obtaining a corrected value of at least one of the plurality of vehicle parameters corresponding to the actual values of each of the vehicle parameters, wherein the corrected value is obtained based on minimizing the difference between the previewed path and the target path to be followed by the vehicle on the road, wherein the target path is obtained based on a plurality of road parameters;
predicting the dynamics of the vehicle based on predicted values of the vehicle speed, the vehicle yaw rate and the input given to a steering wheel of the vehicle; and
repeating the steps of generating the preview of the path, obtaining the corrected value and predicting the dynamics of the vehicle for a predetermined time interval, wherein the steps are repeated after a pre-defined interval of time, and wherein the actual values of at least one of the vehicle speed, the vehicle yaw rate and the input given to the steering wheel of the vehicle is replaced by the predicted values of the vehicle speed, the vehicle yaw rate and the input given to the steering wheel of the vehicle.
12. The method according to claim 11 further comprising dividing a preview distance of the vehicle into a number of sampling segments, wherein the preview distance is a distance for which the method steps are repeated and wherein each of the sampling segments is located at the pre-defined interval of time.
13. The method according to claim 11 wherein generating a preview of the path includes estimating the dynamics and kinematics of the vehicle.
14. The method according to claim 13 wherein estimating the dynamics of the vehicle includes estimating a value of the vehicle lateral velocity, and wherein the value of the vehicle lateral velocity is estimated using the actual values for the vehicle speed, the vehicle yaw rate and the input given to a steering wheel of the vehicle.
15. The method according to claim 13 wherein the estimated kinematics of the vehicle are used to generate the preview of the path to be followed by the vehicle, and wherein the preview is generated by using the value of the vehicle lateral velocity.
16. The method according to claim 11 wherein the actual values of the vehicle speed, the vehicle yaw rate and the input given to the steering wheel of the vehicle are obtained using a sensing module.
17. The method according to claim 11 wherein the target path is obtained using at least one of the groups comprising a global positioning system, a camera and a collection of maps.
18. The method according to claim 11 wherein the dynamics of the vehicle are predicted using a Luenberger state observer and a state integration function, wherein the state integration function uses the predicted values of the at least one of the vehicle speed, the vehicle yaw rate and the input given to the steering wheel of the vehicle.
19. The method according to claim 11 wherein repeating the steps of generating the preview of the path, obtaining the predicted values and predicting the dynamics of the vehicle after a predetermined time interval includes the use of co-ordinate transform techniques.
20. A recursive method for predicting dynamics of a vehicle moving on a road, said dynamics of the vehicle including a plurality of vehicle parameters provided by sensors on the vehicle, said plurality of vehicle parameters including a vehicle speed, a vehicle yaw rate, an input for a steering wheel of the vehicle, said method comprising:
performing the following operations by a processor on the vehicle;
generating a preview of a path to be found by the vehicle on the road where the preview of the path is generated based on actual values of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle, wherein generating a preview of the path includes estimating the dynamics and kinematics of the vehicle including estimating a value of the vehicle lateral velocity using the actual values for the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle, and wherein the estimated kinematics of the vehicle are used to generate the preview or the path to be found by the vehicle where the preview is generated by using the value of the vehicle lateral velocity;
obtaining predicted values of at least one of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle corresponding to the actual values of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle, wherein the predicted values are obtained based on a target path to be followed by the vehicle on the road, and wherein the target path is obtained based on a plurality of road parameters;
obtaining a corrected value of at least one of the plurality of vehicle parameters corresponding to the actual values of each of the vehicle parameters, wherein the corrected value is obtained based on minimizing the difference between the previewed path and the target path to be followed by the vehicle on the road;
predicting the dynamics of the vehicle based on the predicted values of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle;
dividing a preview distance of the vehicle into a number of sampling segments; and
repeating the steps of generating the preview of the path, obtaining the corrected value and predicting the dynamics of the vehicle for a predetermined time interval, wherein the steps are repeated after a pre-defined interval of time, and wherein the actual values of at least one of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle is replaced by the predicted values of the vehicle speed, the vehicle yaw rate and the input for the steering wheel of the vehicle, and wherein the preview distance is a distance for which the method steps are repeated where each of the sampling segments is located at the pre-defined interval of time.