Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications
cs.RO, cs.AI
Submitted: 2024-11-22
Updated: 2026-09-19
Comments: Author replacement
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication
Terminology
Abstract
Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication world, we present Navigation Secure (NavSecure). This vision-based navigation framework merges the strengths of world models with crucial safety-focused decision-making capabilities, enabling autonomous vehicles to navigate real-world complexities securely. Our approach anticipates potential threats and formulates safer routes by harnessing the predictive capabilities of world models, thus significantly reducing the need for extensive real-world trial-and-error learning. Additionally, our method empowers vehicles to autonomously learn and develop through continuous practice, ensuring the system evolves and adapts to new challenges. Incorporating radio frequency technology, NavSecure leverages 5G networks to enhance real-time data exchange, improving communication and responsiveness. Validated through rigorous experiments under simulation-to-real driving conditions, NavSecure has shown exceptional performance in safety-critical scenarios, such as sudden obstacle avoidance. Results indicate that NavSecure excels in key safety metrics, including collision prevention and risk reduction, surpassing other end-to-end methodologies. This framework not only advances autonomous driving safety but also demonstrates how world models can enhance decision-making in critical applications. NavSecure sets a new standard for developing more robust and trustworthy autonomous driving systems, capable of handling the inherent dynamics and uncertainties of real-world environments.
Sources
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
- Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving
- Intervention Design for Effective Sim2Real Transfer
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
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