SYSTEM AND METHOD FOR ADJUSTING TRACK USING SENSOR DATA
A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.
1 . A method comprising:
obtaining a first track associated with an object;
generating, based on the first track, a first set of parameters;
obtaining measurement data from one or more sensors;
extracting a first set of features from the measurement data;
inputting the first set of features to a machine learning model;
executing the machine learning model to output a second set of parameters, wherein the machine learning model has been trained by calculating an adjustment loss of using sample data representing a ground truth track, and updating the machine learning model based on the adjustment loss;
generating, based on the first set of parameters and the second set of parameters, a second track associated with the object; and
providing the second track to an autonomous vehicle control system for autonomous control of a vehicle.
2 . The method of claim 1 , wherein
the first track is associated with a first time, and
the measurement data and the second track are associated with a second time that is later than the first time.
3 . The method of claim 1 , wherein the first set of features comprise at least one of (1) track or automotive vehicle (AV) metadata, (2) lidar points, (3) radar points, or (4) Hough points.
4 . The method of claim 1 , wherein the adjustment loss comprises at least one of translation loss, angle loss, or extent loss.
5 . The method of claim 1 , wherein the machine learning model has been trained by calculating a translation loss, an angle loss, and an extent loss using the sample data representing the ground truth track, and summing the translation loss, the angle loss, and the extent loss using weights to calculate the adjustment loss.
6 . The method of claim 1 , wherein
the first set of parameters defines a first box, and
the second set of parameters defines a second box.
7 . The method of claim 6 , wherein
the second set of parameters define the second box to be consistent with the measurement data.
8 . The method of claim 6 , wherein the second set of parameters comprise at least one of (1) an angle to rotate the first box such that a heading of the first box matches the second box or (2) a translation from the first box to the second box.
9 . The method of claim 6 , wherein
the second set of parameters comprise a translation from the first box to the second box, and
the translation represents a distance between a particular portion of the first box and a particular portion of the second box.
10 . A system comprising one or more processors and one or more memories operably coupled with the one or more processors, wherein the one or more memories store instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining a first track associated with an object;
generating, based on the first track, a first set of parameters;
obtaining measurement data from one or more sensors;
extracting a first set of features from the measurement data;
inputting the first set of features to a machine learning model;
executing the machine learning model to output a second set of parameters, wherein the machine learning model has been trained by calculating an adjustment loss of using sample data representing a ground truth track, and updating the machine learning model based on the adjustment loss;
generating, based on the first set of parameters and the second set of parameters, a second track associated with the object; and
providing the second track to an autonomous vehicle control system for autonomous control of a vehicle.
11 . The system of claim 10 , wherein
the first track is associated with a first time, and
the measurement data and the second track are associated with a second time that is later than the first time.
12 . The system of claim 10 , wherein the first set of features comprise at least one of (1) track or automotive vehicle (AV) metadata, (2) lidar points, (3) radar points, or (4) Hough points.
13 . The system of claim 10 , wherein the adjustment loss comprises at least one of translation loss, angle loss, or extent loss.
14 . The system of claim 10 , wherein the machine learning model has been trained by calculating a translation loss, an angle loss, and an extent loss using the sample data representing the ground truth track, and summing the translation loss, the angle loss, and the extent loss using weights to calculate the adjustment loss.
15 . The system of claim 10 , wherein
the first set of parameters defines a first box, and
the second set of parameters defines a second box.
16 . The system of claim 15 , wherein
the second set of parameters define the second box to be consistent with the measurement data.
17 . The system of claim 15 , wherein the second set of parameters comprise at least one of (1) an angle to rotate the first box such that a heading of the first box matches the second box or (2) a translation from the first box to the second box.
18 . The system of claim 15 , wherein
the second set of parameters comprise a translation from the first box to the second box, and
the translation represents a distance between a particular portion of the first box and a particular portion of the second box.
19 . At least one non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by one or more processors, cause one or more processors to perform operations comprising:
obtaining a first track associated with an object;
generating, based on the first track, a first set of parameters;
obtaining measurement data from one or more sensors;
extracting a first set of features from the measurement data;
inputting the first set of features to a machine learning model;
executing the machine learning model to output a second set of parameters, wherein the machine learning model has been trained by calculating an adjustment loss of using sample data representing a ground truth track, and updating the machine learning model based on the adjustment loss;
generating, based on the first set of parameters and the second set of parameters, a second track associated with the object; and
providing the second track to an autonomous vehicle control system for autonomous control of a vehicle.
20 . The at least one non-transitory computer-readable medium of claim 19 , wherein
the first track is associated with a first time, and
the measurement data and the second track are associated with a second time that is later than the first time.