用大模型提升自动驾驶感知，让车辆更懂环境、更可靠。
Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception
- 将大语言模型融入感知系统，实现环境上下文理解与多传感器融合。
- 实验表明大模型显著提升感知准确率与可靠性，支持更安全决策。
- 适合关注智能驾驶、人机交互与持续学习的开发者和研究者。
自动驾驶车辆依赖复杂的感知系统来理解周围环境，这是安全导航与决策的核心。将大语言模型（LLMs）引入自动驾驶感知框架，为应对动态环境、传感器融合与情境推理等挑战提供了创新方法。本文提出一种新型框架，将LLMs融入感知系统，实现高级情境理解、无缝传感器集成与增强决策支持。实验结果表明，LLMs显著提升了自动驾驶感知系统的准确性和可靠性，为更安全、更智能的自动驾驶技术铺平道路。通过拓展感知的边界，LLMs推动构建更具适应性与人性化特征的驾驶生态，使自动驾驶系统在操作中更可靠、更透明。该进展重塑了人与自动驾驶系统的关系，通过增强理解与个性化决策建立信任。此外，通过整合记忆模块与自适应学习机制，LLMs实现了感知能力的持续进化，使车辆能随时间适应变化的环境与用户偏好。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) rely on sophisticated perception systems to interpret their surroundings, a cornerstone for safe navigation and decision-making. The integration of Large Language Models (LLMs) into AV perception frameworks offers an innovative approach to address challenges in dynamic environments, sensor fusion, and contextual reasoning. This paper presents a novel framework for incorporating LLMs into AV perception, enabling advanced contextual understanding, seamless sensor integration, and enhanced decision support. Experimental results demonstrate that LLMs significantly improve the accuracy and reliability of AV perception systems, paving the way for safer and more intelligent autonomous driving technologies. By expanding the scope of perception beyond traditional methods, LLMs contribute to creating a more adaptive and human-centric driving ecosystem, making autonomous vehicles more reliable and transparent in their operations. These advancements redefine the relationship between human drivers and autonomous systems, fostering trust through enhanced understanding and personalized decision-making. Furthermore, by integrating memory modules and adaptive learning mechanisms, LLMs introduce continuous improvement in AV perception, enabling vehicles to evolve with time and adapt to changing environments and user preferences.
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