Real time management of detected issues
A method for real time management of detected issues, the method includes producing, by a classification unit having a neural network, a classification decision for sensed information obtained in an environment of a vehicle; generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for the sensed information; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification decision; and responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue. The neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.
1. A method for real time management of detected issues, the method comprising:
producing, by a classification unit having a neural network, a classification decision for sensed information obtained in an environment of a vehicle;
generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for the sensed information;
detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification decision; and
responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue;
wherein the neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.
2. The method according to claim 1 , wherein the signature is generated in association with the classification decision and the addressing of the detected issue comprise flagging the signature as being associated with a classification error.
3. The method according to claim 1 , further comprising determining to ignore the detected issue.
4. The method according to claim 1 , comprising addressing the detected issue when the detected issue resulted in an unnecessary movement of the vehicle.
5. The method according to claim 1 , further comprising generating, by the one or more computing devices, a key performance indicator (KPI) report.
6. The method according to claim 1 , further comprising analyzing detected issues over a period of time and marking the detected issues for further downstream analysis.
7. The method according to claim 1 , wherein the addressing the detected issue involves addressing other detected issues that are classifiably similar to the detected issue, using the signature.
8. The method according to claim 1 , wherein the addressing the detected issue involves addressing other detected issues that are classifiably similar to the detected issue, using the signature.
9. The method according to claim 1 , wherein the addressing of the detected issue involves addressing other classification decisions that are classifiably similar to the classification decision, using the signature.
10. The method according to claim 1 , wherein the neural network of the classification unit, another neural network of the one or more computing devices with auto-labeling capabilities are trained with a same training dataset.
11. The method according to claim 1 , further comprising generating the signature for the detected issue using a further neural network that is trained with a same training dataset as the neural network of the classification unit.
12. A non-transitory computer readable medium for real time management of detected issues, the non-transitory computer readable medium that stores instructions for:
producing, by a classification unit having a neural network, a classification decision for sensed information obtained in an environment of a vehicle;
automatically generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for the sensed information;
detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification detection; and
responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue;
wherein the neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.
13. The non-transitory computer readable medium according to claim 12 , wherein the signature is generated in association with the classification decision and the addressing of the detected issue comprise flagging the signature as being associated with a classification error.
14. The non-transitory computer readable medium according to claim 12 , that stores instructions for generating, by the one or more computing devices, a key performance indicator (KPI) report.
15. The non-transitory computer readable medium according to claim 12 , that stores instructions for analyzing detected issues over a period of time and marking the detected issues for further downstream analysis.
16. The non-transitory computer readable medium according to claim 12 , wherein the addressing the detected issue involves addressing other detected issues that are classifiably similar to the detected issue, using the signature.
17. A method for real time management of detected issues, the method comprising:
automatically generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for sensed information obtained in an environment of a vehicle;
detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to a classification detection made for the sensed information using a neural network in a specified state; and
responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue; wherein the neural network in the specified state in the detecting of the issue.
18. The method according to claim 17 , wherein the signature is generated in association with the classification decision and the addressing of the detected issue comprise flagging the signature as being associated with a classification error.
19. The method according to claim 17 , further comprising determining to ignore the detected issue.
20. The method according to claim 17 , comprising addressing the detected issue when the detected issue resulted in an unnecessary movement of the vehicle.
21. The method according to claim 17 , further comprising generating, by the one or more computing devices, a key performance indicator (KPI) report.
22. The method according to claim 17 , further comprising analyzing detected issues over a period of time and marking the detected issues for further downstream analysis.
23. A non-transitory computer readable medium for real time management of detected issues, the non-transitory computer readable medium that stores instructions for:
automatically generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for sensed information obtained in an environment of a vehicle;
detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to a classification detection made for the sensed information using a neural network in a specified state; and
responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue; wherein the neural network in the specified state in the detecting of the issue;
wherein the neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.
24. The non-transitory computer readable medium according to claim 23 , wherein the signature is generated in association with the classification decision and the addressing of the detected issue comprise flagging the signature as being associated with a classification error.
25. The non-transitory computer readable medium according to claim 23 , further storing instructions for determining to ignore the detected issue.
26. The non-transitory computer readable medium according to claim 23 , further storing instructions for addressing the detected issue when the detected issue resulted in an unnecessary movement of the vehicle.
27. The non-transitory computer readable medium according to claim 23 , further storing instructions for generating, by the one or more computing devices, a key performance indicator (KPI) report.
28. The non-transitory computer readable medium according to claim 23 , further storing instructions for analyzing detected issues over a period of time and marking the detected issues for further downstream analysis.