用大模型+规则系统提升自动驾驶紧急刹车的适应性与响应速度
Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking
- 融合多模态大模型与传统规则系统，实现场景理解与快速决策
- 在开放场景下显著提升碰撞风险识别能力，响应速度保持高效
- 首个将多模态大模型用于AEB系统的方案，适合自动驾驶安全研究者
自动紧急制动（AEB）系统是保障自动驾驶车辆乘客安全的关键组件。传统AEB系统主要依赖封闭集感知模块识别交通状况并评估碰撞风险。为增强AEB系统在开放场景下的适应性，我们提出Dual-AEB，该系统结合先进的多模态大语言模型（MLLM）以实现全面的场景理解，并融合传统规则驱动的快速AEB机制以确保响应及时性。据我们所知，Dual-AEB是首个将MLLM引入AEB系统的方案。通过大量实验验证了该方法的有效性。源代码将公开于https://github.com/ChipsICU/Dual-AEB。
原文摘要 · Abstract (English)
Automatic Emergency Braking (AEB) systems are a crucial component in ensuring the safety of passengers in autonomous vehicles. Conventional AEB systems primarily rely on closed-set perception modules to recognize traffic conditions and assess collision risks. To enhance the adaptability of AEB systems in open scenarios, we propose Dual-AEB, a system combines an advanced multimodal large language model (MLLM) for comprehensive scene understanding and a conventional rule-based rapid AEB to ensure quick response times. To the best of our knowledge, Dual-AEB is the first method to incorporate MLLMs within AEB systems. Through extensive experimentation, we have validated the effectiveness of our method. The source code will be available at https://github.com/ChipsICU/Dual-AEB.
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