Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Chaoru Lu;Chenhui Liu
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引用次数: 12

Abstract

Purpose - This paper aims to present a cooperative adaptive cruise control, called stable smart driving model (SSDM), for connected and autonomous vehicles (CAVs) in mixed traffic streams with human-driven vehicles. Design/methodology/approach - Considering the linear stability, SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in. Besides, the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and traffic flows. Under the cut-in condition, the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways. Moreover, single-lane vehicle dynamics are simulated for humandriven vehicles and CAVs. Findings - The result shows that CAVs can reduce platoon-level fuel consumption. SSDM can save the platoon-level fuel consumption up to 15%, outperforming other existing control strategies. Considering the single-lane highway with merging, the higher market penetration of SSDM- equipped CAVs leads to less fuel consumption. Originality/value - The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves. The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.
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考虑线性稳定性的混合交通中协作式自动驾驶汽车的生态控制策略
目的-本文旨在提出一种协作自适应巡航控制,称为稳定智能驾驶模型(SSDM),用于混合交通流中的互联和自动驾驶车辆(CAV)。设计/方法/方法-考虑到线性稳定性,SSDM能够在有或没有切入的情况下为车队提供平稳的减速和加速。此外,本研究还应用校准后的弗吉尼亚理工大学微观能量和排放模型,研究了CAV对车队油耗和交通流量的影响。在切入条件下,考虑到不同的期望时间间隔,SSDM在稳定性方面优于生态SDM和SDM。此外,还模拟了人驾驶车辆和CAV的单车道车辆动力学。研究结果表明,CAV可以降低排级燃料消耗。SSDM可以节省高达15%的排级燃油消耗,优于其他现有的控制策略。考虑到有合流的单车道公路,配备SSDM的CAV的市场渗透率越高,油耗就越低。独创性/价值-所提出的基于规则的控制方法考虑了线性稳定性,以生成更平滑的减速和加速曲线。研究结果有助于制定环境友好型控制策略,为新方法奠定基础。
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Front Cover Contents Advancements and Prospects in Multisensor Fusion for Autonomous Driving Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip
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