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Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives

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  • Nowakowski, Christopher
  • Shladover, Steven E
  • Lu, Xiao-Yun
  • Thompson, Deborah
  • Kailas, Aravind

Abstract

Cooperative Adaptive Cruise Control (CACC) provides an intermediate step toward a longer-term vision of trucks operating in closely-coupled automated platoons. There are important distinctions between CACC and automated truck platooning. First, with CACC, only truck speed control will be automated, using vehicle to vehicle (V2V) communication to supplement forward sensors. The drivers will still be responsible for actively steering the vehicle, lane keeping, and monitoring roadway and traffic conditions. Second, while truck platooning systems have relied on a Constant Distance Gap (CDG) control strategy, CACC has relied on a Constant-Time Gap (CTG) control strategy, where the distance between vehicles is proportional to the speed. For these reasons, a series of trucks using CACC is referred to as a string, rather than a platoon. This report mainly focuses on describing the various CACC operational concept alternatives at the level of individual vehicles, local groups of vehicles and their drivers, and which alternatives should be employed in this research project. These operational concepts can be broken into four categories: string formation, steady-state cruising, string split maneuvers, and faults or abnormal operating conditions.

Suggested Citation

  • Nowakowski, Christopher & Shladover, Steven E & Lu, Xiao-Yun & Thompson, Deborah & Kailas, Aravind, 2015. "Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7jf9n5wm, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt7jf9n5wm
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    References listed on IDEAS

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    3. Lu, Xiao-Yun & Shladover, Steven E, 2011. "Automated Truck Platoon Control," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7c55g2qs, Institute of Transportation Studies, UC Berkeley.
    4. Shladover, Steven E & Lu, Xiao-Yun & Nowakowski, Christopher, 2011. "Development and Assessment of Selected Mobility Applications for VII: Principal Findings," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5r33k6wk, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Sala, Marcel & Soriguera, Francesc, 2021. "Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 116-131.
    2. Zhou, Yang & Wang, Meng & Ahn, Soyoung, 2019. "Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 69-86.
    3. Sun, Xiaotong & Yin, Yafeng, 2021. "Decentralized game-theoretical approaches for behaviorally-stable and efficient vehicle platooning," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 45-69.
    4. Kishore Bhoopalam, A. & van den Berg, R. & Agatz, N.A.H. & Chorus, C.G., 2021. "The long road to automated trucking: Insights from driver focus groups," ERIM Report Series Research in Management ERS-2021-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Larsen, Rune & Rich, Jeppe & Rasmussen, Thomas Kjær, 2019. "Hub-based truck platooning: Potentials and profitability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 249-264.
    6. Zhou, Yang & Ahn, Soyoung, 2019. "Robust local and string stability for a decentralized car following control strategy for connected automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 175-196.
    7. Li, Jie & Fotouhi, Abbas & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2024. "Review on eco-driving control for connected and automated vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Anirudh Kishore Bhoopalam & Niels Agatz & Rob Zuidwijk, 2023. "Platoon Optimization Based on Truck Pairs," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1242-1260, November.
    9. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    10. Zhou, Linjie & Ruan, Tiancheng & Ma, Ke & Dong, Changyin & Wang, Hao, 2021. "Impact of CAV platoon management on traffic flow considering degradation of control mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    11. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    12. Kishore Bhoopalam, A. & Agatz, N.A.H. & Zuidwijk, R.A., 2017. "Planning of Truck Platoons: a Literature Review and Directions for Future Research," ERIM Report Series Research in Management ERS-2017-010-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

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    More about this item

    Keywords

    Engineering; Cooperative Adaptive Cruise Control; CACC; Adaptive Cruise Control; ACC; Speed Control; Truck Platooning; V2V; Vehicle-to-vehicle; Truck strings; DSRC; Platoons;
    All these keywords.

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