论文标题
在信号交叉口学习生态驾驶策略
Learning Eco-Driving Strategies at Signalized Intersections
论文作者
论文摘要
动脉道路中的信号交叉点会导致车辆闲置和过度加速，从而导致燃料消耗和二氧化碳排放。因此，已经有一系列工作研究生态驾驶控制策略，以降低交叉点的燃油消耗和排放水平。但是，制定各种交通设置的有效控制策略的方法仍然难以捉摸。在本文中，我们提出了一种增强学习方法（RL）方法来学习有效的生态驾驶控制策略。我们分析了学习策略对燃料消耗，二氧化碳排放和旅行时间的潜在影响，并与自然主义驾驶和基于模型的基准相比。我们进一步证明了在混合交通情况下学习政策的普遍性。仿真结果表明，连接自动驾驶汽车（CAV）100％渗透的场景可能会降低燃油消耗率高达18％，而二氧化碳排放水平降低了25％，同时甚至提高了旅行速度20％。此外，结果表明，即使是25％的CAV渗透也可以带来至少50％的燃料和减少效益。
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.
