Spatial–temporal transformer-based ecological car-following strategy for connected electric vehicles in dynamic environments

Hao Sun, Shuang Li, Bingbing Li, Mingyang Chen, Sunan Zhang, Weichao Zhuang, Guodong Yin and Boli Chen

Energy, 2025, vol. 317, issue C

Abstract: This study proposes a learning-based ecological car-following strategy for connected and autonomous vehicles (CAVs). Leveraging advanced vehicle-to-vehicle and vehicle-to-infrastructure technologies, CAVs on roads can receive updated traffic flow information and predict the speed profile of the preceding vehicle using a macro–micro fused spatial–temporal transformer. To handle the discrepancies between the predicted and actual speeds of the preceding vehicle and achieve less conservative results, a robust learning-in-the-loop model predictive control algorithm has been developed. Furthermore, to enable real-time computation for practical applications, the system models are transformed from the time domain to the spatial domain, integrating a fictitious control input design for system convexification. Finally, a comprehensive assessment of the proposed strategy is conducted through numerical simulations and on-road vehicle experiments.

Keywords: Car-following strategy; Spatial–temporal transformer; Robust model predictive control; Convex optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:317:y:2025:i:c:s0360544225003159

DOI: 10.1016/j.energy.2025.134673

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