Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation

TL;DR

This paper presents a novel model-based approach for safe autonomous driving that combines constrained policy optimization with latent diffusion models to enhance safety and performance in complex environments.

Contribution

It introduces a new safety-aware policy optimization method using a conditional Value-at-Risk Soft Actor Critic and latent diffusion models for trajectory prediction.

Findings

Outperforms existing methods in safety and efficiency

Effective in complex, high-dimensional environments

Ensures safety even in unpredictable scenarios

Abstract

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.