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An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

Author

Listed:
  • Li, Yun
  • Zhang, Wenshan
  • Zhang, Shengrui
  • Pan, Yingjiu
  • Zhou, Bei
  • Jiao, Shuaiyang
  • Wang, Jianpo

Abstract

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Suggested Citation

  • Li, Yun & Zhang, Wenshan & Zhang, Shengrui & Pan, Yingjiu & Zhou, Bei & Jiao, Shuaiyang & Wang, Jianpo, 2024. "An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    • Handle: RePEc:eee:phsmap:v:641:y:2024:i:c:s0378437124002425
    DOI: 10.1016/j.physa.2024.129733
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    References listed on IDEAS

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