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Soft collision avoidance based car following algorithm for autonomous driving with reinforcement learning

Author

Listed:
  • Zheng, Yuqi
  • Yan, Ruidong
  • Jia, Bin
  • Jiang, Rui
  • Zheng, Shiteng

Abstract

By safety supervision on dangerous driving behaviors, emergent braking in autonomous vehicles can effectively prevent collisions when using the car following algorithm based on deep reinforcement learning. However, the significant deceleration associated with emergent braking often results in an uncomfortable driving experience and high energy consumption. To address this issue, a soft collision avoidance based car following algorithm is proposed. Different from emergent braking, our approach introduces a deceleration adjustment value to the current acceleration output. This adjustment value is calculated by considering safe distance with attenuation coefficient in terms of multi-step prediction, while the attenuation coefficient and the predicted time step are discussed in detail. Comparative analysis, including statistical results and representative cases, demonstrates that the proposed algorithm significantly enhances driving comfort (improve 37.341 %) and reduces energy consumption (improve 11.244 %) without increasing collision risks.

Suggested Citation

  • Zheng, Yuqi & Yan, Ruidong & Jia, Bin & Jiang, Rui & Zheng, Shiteng, 2024. "Soft collision avoidance based car following algorithm for autonomous driving with reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    • Handle: RePEc:eee:phsmap:v:654:y:2024:i:c:s0378437124006460
    DOI: 10.1016/j.physa.2024.130137
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    References listed on IDEAS

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    1. Zhai, Cong & Li, Kening & Zhang, Ronghui & Peng, Tao & Zong, Changfu, 2024. "Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Liu, Huaqing & Jiang, Rui, 2021. "Improving comfort level in traffic flow of CACC vehicles at lane drop on two-lane highways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    3. Nastjuk, Ilja & Herrenkind, Bernd & Marrone, Mauricio & Brendel, Alfred Benedikt & Kolbe, Lutz M., 2020. "What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
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    Cited by:

    1. Zhang, Mingheng & He, Daobin & Gang, Longhui & Wang, Chunqi, 2026. "A deep reinforcement learning approach integrating LSTM-GAT spatiotemporal fusion for autonomous driving decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 681(C).
    2. Zheng, Yuqi & Yan, Ruidong & Jia, Bin & Jiang, Rui & Tapus, Adriana & Chen, Xiaojing & Zheng, Shiteng & Shang, Ying, 2025. "Adaptive hybrid car following strategy using cooperative adaptive cruise control and deep reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 672(C).
    3. Zhang, Lanfang & Gong, Kai & Yin, Xinhe & Fu, Ting & Shangguan, Qiangqiang, 2025. "Development of a car-following model incorporating the oppression effects of large trucks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).

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