Problem: Improve the classification component of an ADAS system to be able to discriminate between the obstacle type (pedestrian, cyclist, child, old person) in

Problem: Improve the classification component of an ADAS system to be able to discriminate between the obstacle type (pedestrian, cyclist, child, old person) in order to adapt the car driver system behavior according to the estimated level of risk. Approach: Train a CNN-based pedestrian classifier using a combination of intensity, depth and flow modalities on the Daimler stereo vision data set. The resulting CNN classifies objects in images as "Pedestrian" or "Non-Pedestrian" (rather than the more granular goal above). Result: Outcomes are measured by the False Positive Rate (FPR) at the 90% True Positive, or detection rate. Lower FPR=better. The CNN created in this experiment ("Late Fusion") achieves an FPR of 0.0125%, which is almost 50% better than the 2010 "Early Fusion" CNN's 0.02% FPR over the same dataset. Still, a 2011 model ("HOG+LBP/MLP, Monolithic HOG classifier") that uses "explicit features" vastly outperforms both, achieving an FPR of 0.00026% over the same dataset. Conclusion: "We showed that the late-fusion classifier outperforms the early-fusion one but does not go beyond the state-of-the-art."