UK‐RAS Robotics & AI Showcase 2024

2024

1. INFOCOM

   Task-Oriented Source-Channel Coding Enabled Autonomous Driving Based on Edge Computing

   Yufeng Diao, Zhen Meng , Xiangmin Xu , and 2 more authors

   In Proceedings of IEEE Conference on Computer Communications Workshops , 2024

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   The communication system is under a paradigm transformation that shifts from traditional bit-level transmission to semantic-level transmission. This transition lays the foundation for complex autonomous driving, necessitating instantaneous processing of substantial data within the constraints of computing capacity and communication bandwidth. In this paper, we propose a novel Task-oriented Source-Channel Coding (TSCC) framework that jointly optimizes source coding and channel coding in a task-oriented manner. Specifically, to reduce communication overhead and guarantee autonomous driving performance, we leverage an autonomous driving agent to guide source-channel coding based on a modified Conditional Variational Autoencoder (CVAE). We test the proposed framework on a well-known autonomous driving platform with different communication channel conditions. The results show that compared to traditional communication and state-of-the-art deep Joint Source-Channel Coding (JSCC), our proposed framework achieves superior performance by saving 98.36% communication overhead and maintains an 83.24% driving score even at 0 dB Signal-to-Noise Ratios (SNR).

   @inproceedings{Diao_2024_TOS,
     author = {Diao, Yufeng and Meng, Zhen and Xu, Xiangmin and She, Changyang and Zhao, Guodong},
     booktitle = {Proceedings of IEEE Conference on Computer Communications Workshops},
     title = {Task-Oriented Source-Channel Coding Enabled Autonomous Driving Based on Edge Computing},
     year = {2024},
     volume = {},
     number = {},
     pages = {1-6},
     keywords = {Joint source-channel coding, AI-driven communication, autonomous driving, edge computing},
     doi = {10.1109/INFOCOMWKSHPS61880.2024.10620735},
   }

2. INFOCOM

   TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation

   Yufeng Diao, Yichi Zhang , Guodong Zhao , and 1 more author

   In Proceedings of IEEE Conference on Computer Communications Workshops , 2024

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   Image-based teleoperation offers significant flexibility and efficiency in several applications, such as teleoperated driving; still, it highly depends on reliable communication band-width and high Signal-to-Noise Ratio (SNR), which is hard to guarantee in uncontrolled environments. This poster tackles the challenge of reliable communication under limited bandwidth. We propose to leverage the context and task knowledge to guide the compression to favor task performance rather than image fidelity. In particular, we jointly designed source-channel coding with a task performer to present an end-to-end TAsk-Guided Image Communication (TAGIC) framework, which uses Soft Introspective Variational Autoencoder (S-IntroVAE) and prioritizes the task-critical image information with limited communication bandwidth in the low SNR region. We demonstrate the effectiveness of TAGIC in a teleoperated driving scenario through the CARLA simulation platform - a widely used simulator in the autonomous driving community. Given the equivalent value of bandwidth compression ratio, TAGIC achieves a 202.6% improvement in the driving score over existing methods at low SNR.

   @inproceedings{Diao_2024_TTI,
     author = {Diao, Yufeng and Zhang, Yichi and Zhao, Guodong and De Martini, Daniele},
     booktitle = {Proceedings of IEEE Conference on Computer Communications Workshops},
     title = {TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation},
     year = {2024},
     volume = {},
     number = {},
     pages = {},
     keywords = {Joint source-channel coding; Teleoperated driving; Machine learning},
     doi = {},
   }