Optimal Driving of Autonomous Vehicle Platoons on Arterial Streets to Reduce Fuel Consumption

Han, Xiao; Ma, Rui; Zhang, Michael · 2020 · ROSA P / Cornell University. Center for Transportation, Environment, and Community Health. (CTECH)

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Summary

This study addresses the problem of excessive fuel consumption and greenhouse gas emissions caused by frequent stops and delays at signalized intersections. While traffic signals are necessary for coordination, improper timing leads to inefficient acceleration and deceleration maneuvers. The authors propose that Connected and Automated Vehicle (CAV) technology, specifically through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, offers a solution by enabling precise trajectory control. The research aims to develop an optimal driving strategy for CAV platoons on arterial streets to minimize total fuel consumption while maintaining safety and throughput. The authors introduce a method called Platoon-Trajectory-Optimization with Gap-Feedback-Control (PTO-GFC). The framework operates in two stages. First, Platoon-Trajectory-Optimization (PTO) treats the entire platoon as a single unit. If the platoon cannot cross the intersection within one green light window, it splits into sub-platoons. The trajectory of the leading vehicle in each sub-platoon is optimized using optimal control theory to minimize fuel consumption, and following vehicles copy this trajectory with minimal time delay and safety gaps. Second, Gap-Feedback-Control (GFC) is applied to merge vehicles with varying initial speeds and headways into these optimal trajectories. The study compares PTO-GFC against two baseline methods: Leading-Trajectory-Optimization (LTO), where only the lead vehicle is optimized and others follow a car-following model, and Aggressive Trajectory (AT), where all vehicles drive at maximum speed and stop for red lights. The authors also extend the analysis to multiple interacting platoons. Numerical simulations and sensitivity analyses demonstrate that PTO-GFC significantly outperforms both LTO and AT. In single-platoon scenarios, PTO-GFC reduced fuel consumption by approximately 3.13% compared to LTO and by 27.7% compared to AT when vehicles could pass without stopping. The method also reduced travel time, showing improvements of roughly 25.4% over AT. Sensitivity analysis revealed that PTO-GFC performs best when vehicles have sufficient control space (distance to the intersection) and lower maximum speeds. The method remained effective even when initial platoon conditions varied in speed and headway. In multi-platoon scenarios, PTO-GFC reduced fuel consumption by more than 30% and travel time by over 20% compared to the baseline methods. The benefits were consistent across different fuel consumption models (CAITR and VT-micro) and varying inter-platoon arrival rates. The findings highlight the potential of CAV technology to mitigate the environmental impact of urban transportation. By smoothing trajectories and avoiding idling, PTO-GFC reduces both congestion and emissions. The study concludes that optimizing the entire platoon as a cohesive unit, rather than controlling vehicles individually or relying on human-driven car-following behaviors, yields superior efficiency. This approach provides a robust framework for integrating autonomous vehicles into existing signalized infrastructure to achieve significant energy savings.

Key finding

The PTO-GFC method reduced fuel consumption by approximately 3.13% compared to LTO and by about 27.7% compared to AT in case studies where platoons could not clear the intersection within a single green light window.

Methodology

modeling

Provenance

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• eco driving