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LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
arXiv – CS AI|Can Cui, Yunsheng Ma, Sung-Yeon Park, Zichong Yang, Yupeng Zhou, Peiran Liu, Juanwu Lu, Juntong Peng, Jiaru Zhang, Ruqi Zhang, Lingxi Li, Yaobin Chen, Jitesh H. Panchal, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Ziran Wang|
🤖AI Summary
Researchers have published a comprehensive review of Large Language Models for Autonomous Driving (LLM4AD), introducing new benchmarks and conducting real-world experiments on autonomous vehicle platforms. The paper explores how LLMs can enhance perception, decision-making, and motion control in self-driving cars, while identifying key challenges including latency, security, and safety concerns.
Key Takeaways
- →LLM4AD represents a novel approach to integrating large language models into autonomous driving systems for enhanced reasoning and decision-making capabilities.
- →New benchmarks including LaMPilot-Bench and CARLA Leaderboard 1.0 have been proposed to evaluate instruction-following abilities of LLM4AD systems.
- →Real-world experiments demonstrate both on-cloud and on-edge LLM deployment possibilities for personalized autonomous vehicle control.
- →The proposed ViLaD framework explores integrating vision-language diffusion models into autonomous driving applications.
- →Key implementation challenges include latency optimization, security concerns, safety validation, and personalization requirements.
#llm#autonomous-driving#ai-research#benchmarking#computer-vision#real-world-testing#diffusion-models#edge-computing
Read Original →via arXiv – CS AI
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