Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
This work addresses safety for autonomous driving systems, representing an incremental improvement with novel integration of methods for known bottlenecks.
The research tackled safety issues in autonomous driving by proposing a model-based policy optimization method using conditional Value-at-Risk based Soft Actor Critic and latent diffusion models, which outperformed existing methods in safety, efficiency, and decision-making in simulated and real environments.
With the advancement of autonomous driving, ensuring safety during motion planning and navigation is becoming more and more important. However, most end-to-end planning methods suffer from a lack of safety. This research addresses the safety issue in the control optimization problem of autonomous driving, formulated as Constrained Markov Decision Processes (CMDPs). We propose a novel, model-based approach for policy optimization, utilizing a conditional Value-at-Risk based Soft Actor Critic to manage constraints in complex, high-dimensional state spaces effectively. Our method introduces a worst-case actor to guide safe exploration, ensuring rigorous adherence to safety requirements even in unpredictable scenarios. The policy optimization employs the Augmented Lagrangian method and leverages latent diffusion models to predict and simulate future trajectories. This dual approach not only aids in navigating environments safely but also refines the policy's performance by integrating distribution modeling to account for environmental uncertainties. Empirical evaluations conducted in both simulated and real environment demonstrate that our approach outperforms existing methods in terms of safety, efficiency, and decision-making capabilities.