MMCD: 知識蒸留を用いたマルチモーダル協調意思決定によるコネクテッド自律走行
MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation
複数車両のマルチモーダル観測を融合し、知識蒸留で一部センサー欠如時にも堅牢な意思決定を実現する枠組みを提案。地上車・空陸協調実験で安全性を最大20.7%改善。
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著者: Rui Liu, Zikang Wang, Peng Gao, Yu Shen, Pratap Tokekar, Ming Lin
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
Autonomous systems have advanced significantly, but challenges persist in accident-prone environments where robust decision-making is crucial. A single vehicle's limited sensor range and obstructed views increase the likelihood of accidents. Multi-vehicle connected systems and multi-modal approaches, leveraging RGB images and LiDAR point clouds, have emerged as promising solutions. However, existing methods often assume the availability of all data modalities and connected vehicles during both training and testing, which is impractical due to potential sensor failures or missing connected vehicles. To address these challenges, we introduce a novel framework MMCD (Multi-Modal Collaborative Decision-making) for connected autonomy. Our framework fuses multi-modal observations from ego and collaborative vehicles to enhance decision-making under challenging conditions. To ensure robust performance when certain data modalities are unavailable during testing, we propose an approach based on cross-modal knowledge distillation with a teacher-student model structure. The teacher model is trained with multiple data modalities, while the student model is designed to operate effectively with reduced modalities. In experiments on $\textit{connected autonomous driving with ground vehicles}$ and $\textit{aerial-ground vehicles collaboration}$, our method improves driving safety by up to ${\it 20.7}\%$, surpassing the best-existing baseline in detecting potential accidents and making safe driving decisions. More information can be found on our website https://ruiiu.github.io/mmcd.
