The task of following-the-leader is implemented using a hierarchical Deep Neural Network (DNN) end-to-end driving model to match the direction and speed of a target pedestrian. The model uses a classifier DNN to determine if the pedestrian is within the field of view of the camera sensor. If the pedestrian is present, the image stream from the camera is fed to a regression DNN which simultaneously adjusts the autonomous vehicle's steering and throttle to keep cadence with the pedestrian. If the pedestrian is not visible, the vehicle uses a straightforward exploratory search strategy to reacquire the tracking objective. The classifier and regression DNNs incorporate grouped convolutions to boost model performance as well as to significantly reduce parameter count and compute latency. The models are trained on the Intelligence Processing Unit (IPU) to leverage its fine-grain compute capabilities in order to minimize time-to-train. The results indicate very robust tracking behavior on the part of the autonomous vehicle in terms of its steering and throttle profiles, which required minimal data collection to produce. The throughput in terms of processing training samples has been boosted by the use of the IPU in conjunction with grouped convolutions by a factor \sim 3.5 for training of the classifier and a factor of \sim 7 for the regression network. A recording of the vehicle tracking a pedestrian has been produced and is available on the web.
The task of following-the-leader is implemented using a hierarchical Deep Neural Network (DNN) end-to-end driving model to match the direction and speed of a target pedestrian. The model uses a classifier DNN to determine if the pedestrian is within the field of view of the camera sensor. If the pedestrian is present, the image stream from the camera is fed to a regression DNN which simultaneously adjusts the autonomous vehicle's steering and throttle to keep cadence with the pedestrian. If the pedestrian is not visible, the vehicle uses a straightforward exploratory search strategy to reacquire the tracking objective. The classifier and regression DNNs incorporate grouped convolutions to boost model performance as well as to significantly reduce parameter count and compute latency. The models are trained on the Intelligence Processing Unit (IPU) to leverage its fine-grain compute capabilities in order to minimize time-to-train. The results indicate very robust tracking behavior on the part of the autonomous vehicle in terms of its steering and throttle profiles, while requiring minimal data collection to produce. The throughput in terms of processing training samples has been boosted by the use of the IPU in conjunction with grouped convolutions by a factor ~ 3.5 for training of the classifier and a factor of ~ 7 for the regression network. A recording of the vehicle tracking a pedestrian has been produced and is available on the web.
R
which required
while requiring
R
\sim
~
R
\sim
~