Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving

• URL: http://arxiv.org/abs/2310.00013v3
• Date: Sat, 16 Mar 2024 15:20:43 GMT
• Title: Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
• Authors: Senkang Hu, Zhengru Fang, Haonan An, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang,
• Abstract summary: We propose ACC-DA, a channel-aware collaborative perception framework. We first design a transmission delay minimization method, which can construct the communication graph. We then propose an adaptive data reconstruction mechanism, which can dynamically adjust the rate-distortion trade-off to enhance perception efficiency.
• Score: 21.11621380546942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via communications. Despite advances in previous approaches, challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity. Our novelties lie in three aspects. We first design a transmission delay minimization method, which can construct the communication graph and minimize the transmission delay according to different channel information state. We then propose an adaptive data reconstruction mechanism, which can dynamically adjust the rate-distortion trade-off to enhance perception efficiency. Moreover, it minimizes the temporal redundancy during data transmissions. Finally, we conceive a domain alignment scheme to align the data distribution from different vehicles, which can mitigate the domain gap between different vehicles and improve the performance of the target task. Comprehensive experiments demonstrate the effectiveness of our method in comparison to the existing state-of-the-art works.

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