Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework
This work addresses pedestrian safety for distracted drivers, but it is incremental as it combines existing methods for detection and prediction.
The authors tackled pedestrian collision avoidance by developing a deep learning framework that detects pedestrians and predicts driver drowsiness using two CNvolutional Neural Networks, achieving promising results on a low-cost Raspberry Pi 3 Model B+.
We propose a new deep learning based framework to identify pedestrians, and caution distracted drivers, in an effort to prevent the loss of life and property. This framework uses two Convolutional Neural Networks (CNN), one which detects pedestrians and the second which predicts the onset of drowsiness in a driver, is implemented on a Raspberry Pi 3 Model B+, shows great promise. The algorithm for implementing such a low-cost, low-compute model is presented and the results discussed.