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arXiv · 2410.08616

Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking

Wei Zhang·Pengfei Li·Junli Wang·Bingchuan Sun·Qihao Jin·Guangjun Bao·Shibo Rui·Yang Yu·Wenchao Ding·Peng Li·Yilun Chen
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Abstract

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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Wei Zhang, Pengfei Li, Junli Wang, Bingchuan Sun, Qihao Jin, Guangjun Bao, Shibo Rui, Yang Yu, Wenchao Ding, Peng Li, Yilun Chen. 2024-10-11. Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking. https://arxiv.org/abs/2410.08616

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