A Computer Vision-Based Attention Generator using DQN

A significant obstacle to achieving autonomous driving (AD) and advanced driver-assistance systems (ADAS) functionality in passenger vehicles is high-fidelity perception at a sufficiently low cost of computation and sensors. An area of research that aims to address this challenge takes inspiration from human foveal vision by using attention-based sensing. This work presents an end-to-end computer vision-based Deep Q-Network (DQN) technique that intelligently selects a priority region of an image to place greater attention to achieve better perception performance. This method is evaluated on the Berkeley Deep Drive (BDD) dataset. Results demonstrate that a substantial improvement in perception performance can be attained – compared to a baseline method – at a minimal cost in terms of time and processing.