Method for real time analysis
A method for real time analysis, the method includes producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle; automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, an automated ground truth labeling for a determined set of sensed information units; 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 the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue. The neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.
1 . A method for real time analysis, the method comprising:
producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle;
automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, a automated ground truth labeling for a determined set of sensed information units;
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 the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue,
wherein the neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.
2 . The method according to claim 1 , wherein the signature is generated in association with the classification decision and the addressing 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 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.
12 . A non-transitory computer readable medium for real time analysis, the non-transitory computer readable medium stores instructions for:
producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle;
automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, a automated ground truth labeling for a determined set of sensed information units;
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 the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue, wherein the neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the 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 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 , further that stores instructions for determining to ignore the detected issue.
15 . The non-transitory computer readable medium according to claim 12 , that stores instructions for addressing the detected issue when the detected issue resulted in an unnecessary movement of the vehicle.
16 . The non-transitory computer readable medium according to claim 12 , further that stores instructions for generating, by the one or more computing devices, a key performance indicator (KPI) report.
17 . The non-transitory computer readable medium according to claim 12 , further that stores instructions for analyzing detected issues over a period of time and marking the detected issues for further downstream analysis.
18 . 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.
19 . 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.
20 . The non-transitory computer readable medium according to claim 12 , wherein the addressing the detected issue involves addressing other classification decisions that are classifiably similar to the classification decision, using the signature.