Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks

TL;DR

This paper introduces a decentralized semantic traffic control system for autonomous vehicles that uses deep reinforcement learning with DQN to make real-time lane decisions around roadblocks, reducing reliance on server processing.

Contribution

It proposes a novel decentralized framework where AVs encode semantics locally and employ RL with DQN for dynamic traffic management around roadblocks, enhancing real-time decision-making.

Findings

Effective RL-based decision-making for lane changes near roadblocks.

Reduced data transmission by decentralizing semantic encoding.

Demonstrated viability of DQN in dynamic traffic scenarios.

Abstract

Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions.