An Autonomous Driving System with CARLA using Reinforcement Learning

Engineering notes

Key topics: autonomous driving system, autonomous driving, end-to-end, reinforcement learning, carla, deployment, perception, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Developing systems capable of driving autonomously demands not only robust perception of the surrounding environment but also reliable decision-making under real-world uncertainty. Since deploying untested controllers on public roads carries significant risk and cost, simulation platforms such as CARLA have become the standard staging ground for early-stage research. In this work, we trained an end-to-end steering controller inside the CARLA simulator using Deep Q-Network (DQN) reinforcement learning, and subsequently evaluated a Double DQN (DDQN) variant that mitigates the overestimation bias inherent in vanilla DQN. Both agents process raw camera frames from a forward-facing sensor to produce discrete steering commands, while a separate PID speed controller handles longitudinal velocity. Positive rewards encourage smooth lane-following and target-speed maintenance; collision and lane-departure events trigger penalty signals that guide the agent away from unsafe behaviour. Stabilization mechanisms experience replay, epsilon-greedy exploration decay, and a periodically synchronized target network were applied throughout training. Our experiments confirm that DQN agents can acquire competent steering policies from pixels alone, and that the DDQN extension yields measurably lower collision rates and more consistent trajectories. The results support the broader applicability of deep reinforcement learning to autonomous steering tasks and lay groundwork for future extensions toward realworld deployment.

Links and sources

• Official / arXiv page

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