WEBLIGHT: DRL based Intersection Control in Developing Countries with Reliable Cameras
Keywords: RL, DRL, Traffic Signal Control
Abstract: Effective traffic intersection control is crucial for urban sustainability. State of the art research seeking Artificial Intelligence (AI), for example Deep Reinforcement Learning (DRL) based traffic control requires environment states through various Computer Vision methods, where the collective state of multiple cameras across an intersection constitute the single state for AI. This brings in serious robustness or fault-tolerance concerns on the deployed system. Camera systems are highly susceptible to faults due to multiple possible points of failure. A single fault collapses the AI state and hence the capacity of AI controller to manage the traffic is gone. Also, infrastructure deployment and maintenance is a slow bureaucratic process in these countries, which makes camera faults a regular event. In the given paper, we build a web based, independent and alternative, traffic state processing method which can replace the camera dependency completely, or support as a backup mechanism until the camera system is back online, making the AI intersection control robust to camera failures.
Submission Number: 12
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