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Effective Vehicle Lane-Change Sensing Using Onboard Smartphone Based on Temporal Convolutional Network

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Algorithms and Architectures for Parallel Processing (ICA3PP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13777))

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Abstract

Lane-change sensing is one of the fundamental requirements to enable autonomous driving and safety-critical Intelligent Transportation System (ITS) applications. This work presents a deep-learning approach for detecting the lane changing of vehicles using onboard smartphone, aiming at achieving low-cost and scalable sensing and complementing computer vision-based solutions in special traffic conditions such as heavy fog weather. Specifically, we first present a lane-change sensing framework based on accelerometer and gyroscope readings from the onboard smartphone, which supports offline trajectory data collection and training, as well as online real-time lane-change sensing. Second, in light of the fact that Temporal Convolutional Network (TCN) is computational-efficiency for sequential tasks, we propose a TCN-based Lane-Change Sensing (TCN-LCS) algorithm, which consists of a dynamic sequence length adaptation method for offline training, and a sliding window inference strategy for online inference. Finally, we build the system prototype and give an extensive performance evaluation in real-world traffic environments. The experimental results conclusively demonstrate the feasibility and efficiency of the proposed framework and solution.

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172064, the Chongqing Young-Talent Program (Project No. cstc2022ycjh-bgzxm0039), the Venture & Innovation Support Program for Chongqing Overseas Returnees (Project No. cx2021063) and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJQN202100637).

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Authors and Affiliations

  1. Chongqing University, Chongqing, 400044, China

    Junbo Hu, Kai Liu, Feiyu Jin, Guozhi Yan & Songtao Guo

  2. Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hao Zhang

  3. Nanfang College Guangzhou, Guangzhou, 510970, China

    Kai Liu & Hu Min

Authors
  1. Junbo Hu
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  2. Kai Liu
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  3. Feiyu Jin
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  4. Guozhi Yan
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  5. Hao Zhang
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  6. Songtao Guo
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  7. Hu Min
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Corresponding author

Correspondence to Kai Liu .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  2. University of New Brunswick, Fredericton, NB, Canada

    Rongxing Lu

  3. University of Exeter, Exeter, UK

    Geyong Min

  4. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

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Hu, J. et al. (2023). Effective Vehicle Lane-Change Sensing Using Onboard Smartphone Based on Temporal Convolutional Network. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-22677-9_8

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