Robust and Adaptive Traffic Signal Control for Unstructured Driving Scenarios in the Developing World

s- Adaptive traffic signal control (ATSC) systems are key to efficient traffic management in smart cities. While RADAR or inductive loop based systems are regularly used for the task in the developed world, financial and infrastructure constraints precludes use of sophisticated and expensive sensors in the developing countries. RGB cameras are cheap and easy to deploy, making their usage attractive for ATSC. However, unstructured driving scenarios, and the requirement to work 24×7 pose a significant challenge to the camera based systems. In this paper we propose to use traffic tailback length (TTL) for training a Deep Reinforcement Learning (RL) Agent in an ATSC system. We show that TTL can be calculated by simply performing a `background subtraction' on the input video captured from an RGB camera. The background subtraction operation is well studied in computer vision and can be robustly performed in low illumination, and rainy, snowy, or hazy weather. The operation does not require detecting vehicles explicitly, thus making the proposed system applicable in unstructured driving conditions, involving unseen vehicles. We show, in a simulated environment using SUMO, that our technique outperform the state of the art, which uses explicit vehicle detection information as well as speed of the vehicle, in terms of vehicle throughput, queue length, and average delay.