Rain-Aware Lane Change Decision Model for Autonomous Vehicle Using Deep Reinforcement Learning

Abstract Autonomous vehicles (AVs) have shown significant potential in recent years, with increasing interest from the public, industry, and academia in their adoption on roads. To achieve reliable autonomous driving, AVs must be able to operate safely under adverse weather conditions such as rain-induced wet roads which pose serious safety risks to road users. Therefore, AVs require a robust Lane Change Decision (LCD) model as a crucial part of their decision-making system. Although existing studies have explored the use of deep reinforcement learning (DRL) for LCD models, most of these works do not consider rainy conditions in the development of the LCD model. To address this gap, this paper proposes a rain-aware LCD model using Deep Q-Networks (DQN). The proposed framework was implemented using the open-source Highway-env simulation environment, modified to incorporate rain-dependent human-driven vehicle behavior and wet-road friction effects on the ego vehicle. In addition, a safety-augmented DQN model was introduced to penalize unsafe close-following behavior before an actual collision occurs. The proposed models were evaluated in terms of safety, efficiency, comfort, and global traffic impact against rule-based (MOBIL) and learning-based benchmarks. The proposed Rain-aware DQN model improved the overall lane-changing performance by 11.2% and 17.7% according to two composite evaluation metrics, compared with MOBIL. The proposed Safety-augmented DQN model also achieved strong performance, with corresponding improvements of 10.6% and 17.3%. Moreover, comparison with other DQN models showed that the proposed Rain-aware DQN improved the safety index by 2.1% and 3.0% respectively.