  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.
