Enhancing Autonomous Driving Safety through World Model-Based Predictive Navigation and Adaptive Learning Algorithms for 5G Wireless Applications
Hong Ding, Ziming Wang, Yi Ding, Hongjie Lin, SuYang Xi, Chia Chao, Kang

TL;DR
This paper introduces NavSecure, a world model-based framework that enhances autonomous driving safety by predicting threats, optimizing routes, and leveraging 5G communication for real-time data exchange, validated through simulation experiments.
Contribution
The paper presents a novel navigation system combining world models with safety-focused decision-making and 5G technology, improving autonomous driving safety and adaptability.
Findings
NavSecure reduces collision risk in safety-critical scenarios.
The system outperforms existing methods in safety metrics.
Real-time data exchange via 5G enhances responsiveness.
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…
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Taxonomy
TopicsSmart Systems and Machine Learning · Advanced MIMO Systems Optimization
