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Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments

Author

Listed:
  • Li, Tienan
  • Chen, Danjue
  • Zhou, Hao
  • Laval, Jorge
  • Xie, Yuanchang

Abstract

Emerging automated vehicle (AV) technologies are increasingly being deployed around the world and it is only a matter of time before the transportation landscape changes dramatically. Unfortunately, those changes cannot be well predicted due to the lack of empirical data. But adaptive cruise control (ACC) vehicles are common in the market and can be used to fill this gap. In this paper, we aim to characterize the empirical car-following behaviors of a commercial ACC system and understand how ACC behaves in different conditions and the underlying impact mechanism. It is found that for a single ACC: (i) the ACC response time is comparable to human drivers but much larger than the ACC controller time gap and it exhibits small variance, (ii) the ACC response can amplify or dampen an oscillation, (iii) after the oscillation, the stabilization process can exhibit overshooting or undershooting, and (iv) these CF behaviors depend largely on the ACC headway setting, speed level, and leader stimulus, which produce the impacts directly and/or indirectly through the mediation of earlier ACC behaviors. For a three-vehicle platoon, our main finding is that the change from one ACC vehicle to the next is progressive for oscillation growth, and regressive for deceleration, acceleration, and overshooting. This implies that in long platoons, oscillation amplitude tends to exacerbate very quickly, which forces ACC vehicles further upstream to apply very strong braking followed by a strong acceleration. This can cause significant overshooting and safety hazards.

Suggested Citation

  • Li, Tienan & Chen, Danjue & Zhou, Hao & Laval, Jorge & Xie, Yuanchang, 2021. "Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 67-91.
  • Handle: RePEc:eee:transb:v:147:y:2021:i:c:p:67-91
    DOI: 10.1016/j.trb.2021.03.003
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    References listed on IDEAS

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    Cited by:

    1. Wen, Jianghui & Hong, Lijiang & Dai, Min & Xiao, Xinping & Wu, Chaozhong, 2023. "A stochastic model for stop-and-go phenomenon in traffic oscillation: On the prospective of macro and micro traffic flow," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    2. Shi, Xiaowei & Li, Xiaopeng, 2023. "Trajectory Planning for an Autonomous Vehicle with Conflicting Moving Objects Along a Fixed Path – An Exact Solution Method," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 228-246.
    3. Li, Chao & Zhao, Xiaomei & Xie, Dongfan, 2022. "Steady-state performance and dynamic performance of heterogeneous platoons under a connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    4. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Ding, Heng & Pan, Hao & Bai, Haijian & Zheng, Xiaoyan & Chen, Jin & Zhang, Weihua, 2022. "Driving strategy of connected and autonomous vehicles based on multiple preceding vehicles state estimation in mixed vehicular traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).

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