Data driven customization of driver assistance system
A computer-implemented method comprises: receiving first telemetry data generated by sensors of respective first vehicles in a fleet; clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet; receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver; associating the second driver with a first group of the groups by classifying the received second telemetry data; providing a subset of the first telemetry data corresponding to the first cluster as a baseline dataset for training of machine learning algorithms; generating baseline tuning parameter values using the trained machine learning algorithms; and providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.
1. A computer-implemented method comprising:
receiving first telemetry data generated by sensors of respective first vehicles in a fleet;
projecting the received first telemetry data into an abstract space, wherein projecting the received first telemetry data into the abstract space comprises combining first and second values having different physical units with each other to form a third value;
clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet;
receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver;
associating the second driver with a first group of the groups by classifying the received second telemetry data;
providing a subset of the first telemetry data corresponding to the first group as a baseline dataset for training of machine learning algorithms;
generating baseline tuning parameter values using the trained machine learning algorithms; and
providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.
2. The computer-implemented method of claim 1 , further comprising filtering the first telemetry data to filtered telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the filtered telemetry data into the groups.
3. The computer-implemented method of claim 1 , further comprising augmenting the first telemetry data to augmented telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the augmented telemetry data into the groups.
4. The computer-implemented method of claim 1 , further comprising performing dimension reduction on the received first telemetry data to generate dimension-reduced telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the dimension-reduced telemetry data into the groups.
5. The computer-implemented method of claim 4 , wherein performing the dimension reduction on the received first telemetry data comprises projecting the received first telemetry data into the abstract space.
6. The computer-implemented method of claim 5 , wherein classifying the received second telemetry data comprises projecting the received second telemetry data into the abstract space.
7. The computer-implemented method of claim 1 , wherein the baseline tuning parameter values control at least one aspect of the driver assistance system, the aspect including one or more of a distance between the third vehicle and an object, a speed of the third vehicle, a trajectory of the third vehicle, or an acceleration of the third vehicle.
8. The computer-implemented method of claim 1 , wherein the third vehicle is the second vehicle.
9. The computer-implemented method of claim 1 , wherein receiving the second telemetry data includes performing event detection to record a specific scenario.
10. The computer-implemented method of claim 1 , wherein clustering the first telemetry data into the groups comprises specifying how many the groups must be.
11. The computer-implemented method of claim 1 , further comprising, before providing the baseline dataset for the training of the machine learning algorithms, obfuscating an association between the baseline dataset and the second driver.
12. The computer-implemented method of claim 11 , wherein obfuscating the association between the baseline dataset and the second driver comprises applying a hash function to (i) a vehicle identification number of the second vehicle and to (ii) a user identifier for the second driver.
13. The computer-implemented method of claim 1 , further comprising training a feature generation algorithm using the second telemetry data.
14. The computer-implemented method of claim 13 , wherein the feature generation algorithm is trained to generate a parameter value candidate for at least one of (i) a time gap; (ii) a lane change duration parameter; (iii) a distance from an exit to a lane change; (iv) a lane bias; (v) in a lateral direction, a velocity, acceleration, or jerk; or (vi) a parking distance.
15. The computer-implemented method of claim 1 , wherein the machine learning algorithms include at least one of a regression algorithm or a classification algorithm.
16. The computer-implemented method of claim 1 , wherein combining the first and second values with each other to form the third value comprises adding a dimension to the first telemetry data that does not have real-world significance.
17. The computer-implemented method of claim 16 , wherein the different physical units are distance and speed, and wherein combining the first and second values with each other comprises adding the first and second values to each other.
18. A computer program product tangibly embodied in a non-transitory storage medium, the computer program product including instructions that when executed cause a processor to perform operations, the operations comprising:
receiving first telemetry data generated by sensors of respective first vehicles in a fleet;
projecting the received first telemetry data into an abstract space, wherein projecting the received first telemetry data into the abstract space comprises combining first and second values having different physical units with each other to form a third value;
clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet;
receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver;
associating the second driver with a first group of the groups by classifying the received second telemetry data;
providing a subset of the first telemetry data corresponding to the first group as a baseline dataset for training of machine learning algorithms;
generating baseline tuning parameter values using the trained machine learning algorithms; and
providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.
19. The computer program product of claim 18 , the operations further comprising filtering the first telemetry data to filtered telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the filtered telemetry data into the groups.
20. The computer program product of claim 18 , the operations further comprising augmenting the first telemetry data to augmented telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the augmented telemetry data into the groups.
21. The computer program product of claim 18 , the operations further comprising performing dimension reduction on the received first telemetry data to generate dimension-reduced telemetry data, wherein clustering the first telemetry data into the groups comprises clustering the dimension-reduced telemetry data into the groups.
22. The computer program product of claim 18 , the operations further comprising, before providing the baseline dataset for the training of the machine learning algorithms, obfuscating an association between the baseline dataset and the second driver.
23. The computer program product of claim 18 , the operations further comprising training a feature generation algorithm using the second telemetry data.