Dilu: A knowledge-driven approach to autonomous driving with large language models

[PDF] arxiv.org

Position: Foundation Agents as the Paradigm Shift for Decision Making

X Liu, X Lou, J Jiao, J Zhang - arXiv preprint arXiv:2405.17009, 2024 - arxiv.org
Decision making demands intricate interplay between perception, memory, and reasoning to
discern optimal policies. Conventional approaches to decision making face challenges …
被引用次数：1 相关文章 所有 3 个版本
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Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

S Atakishiyev, M Salameh, R Goebel - arXiv preprint arXiv:2403.12176, 2024 - arxiv.org
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing
development of highly autonomous vehicles, largely due to advances in deep learning, the …
被引用次数：1 相关文章 所有 2 个版本
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Asynchronous Large Language Model Enhanced Planner for Autonomous Driving

Y Chen, Z Ding, Z Wang, Y Wang, L Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Despite real-time planners exhibiting remarkable performance in autonomous driving, the
growing exploration of Large Language Models (LLMs) has opened avenues for enhancing …
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Exploring Backdoor Attacks against Large Language Model-based Decision Making

R Jiao, S Xie, J Yue, T Sato, L Wang, Y Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) have shown significant promise in decision-making tasks
when fine-tuned on specific applications, leveraging their inherent common sense and …
相关文章 所有 2 个版本
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Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models

Y Yang, Q Zhang, K Ikemura, N Batool… - arXiv preprint arXiv …, 2024 - arxiv.org
Addressing hard cases in autonomous driving, such as anomalous road users, extreme
weather conditions, and complex traffic interactions, presents significant challenges. To …
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PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning

Y Zheng, Z Xing, Q Zhang, B Jin, P Li, Y Zheng… - arXiv preprint arXiv …, 2024 - arxiv.org
Vehicle motion planning is an essential component of autonomous driving technology.
Current rule-based vehicle motion planning methods perform satisfactorily in common …
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LORD: Large Models based Opposite Reward Design for Autonomous Driving

X Ye, F Tao, A Mallik, B Yaman, L Ren - arXiv preprint arXiv:2403.18965, 2024 - arxiv.org
Reinforcement learning (RL) based autonomous driving has emerged as a promising
alternative to data-driven imitation learning approaches. However, crafting effective reward …
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Pre-trained Transformer-Enabled Strategies with Human-Guided Fine-Tuning for End-to-end Navigation of Autonomous Vehicles

D Hu, C Huang, J Wu, H Gao - arXiv preprint arXiv:2402.12666, 2024 - arxiv.org
Autonomous driving (AD) technology, leveraging artificial intelligence, strives for vehicle
automation. End-toend strategies, emerging to simplify traditional driving systems by …
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AccidentGPT: A V2X Environmental Perception Multi-modal Large Model for Accident Analysis and Prevention

L Wang, Y Ren, H Jiang, P Cai, D Fu… - 2024 IEEE Intelligent …, 2024 - ieeexplore.ieee.org
Traffic accidents are a significant factor leading to injuries and property losses, prompting
extensive research in the field of traffic safety. However, previous studies, whether focused …
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A Survey of Language-Based Communication in Robotics

W Hunt, SD Ramchurn, MD Soorati - arXiv preprint arXiv:2406.04086, 2024 - arxiv.org
Embodied robots which can interact with their environment and neighbours are increasingly
being used as a test case to develop Artificial Intelligence. This creates a need for …
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