Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns
Dongyang Li, Ehsan Javanmardi, Naren Bao, Manabu Tsukada · 2024
Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles.It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle's current state to make safe decisions.A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations.While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle's states are crucial to the scenario because there might be arbitrary optimal actions under different states.To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle's states and its perception of the environment.We evaluate our model in AWSIM, an open-source autonomous driving simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.