This paper presents the energy-saving potential of early-stage autonomous vehicles (AVs) by analyzing empirical AV driving records from a public road test. Vehicle-specific power-based microscopic energy consumption models are used to estimate the energy efficiency of the tested AVs, including light-duty vehicles and heavy-duty trucks. Overall, AVs do not necessarily save energy, largely depending on driving scenarios, such as traffic conditions and road types. On expressways, autonomous driving (AD) has an insignificant energy-saving effect (within 10 %) due to similar characteristics to human driving (HD). On urban roads with lower traffic speeds and more interventions from surrounding traffic, AD performs higher energy efficiency due to its capability to accelerate more smoothly by up to 60 %; however, frequent human disengagement generates additional energy consumption of 8%–40 %, especially on urban arterials. We further assessed the marginal effect of trip average speed and AD ratio on energy efficiency, illustrating varied relationships for different driving scenarios and power types. The study suggests a more moderate AD acceleration to elevate the energy-saving effect. Nevertheless, applying AD can hardly reduce the energy of early-stage AVs; instead, control methods to maintain an appropriate speed of the mixed traffic flow can help decrease the total energy consumption.

中文翻译：

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早期自动驾驶汽车的节能效果：大都市区的案例研究和建议

本文通过分析公共道路测试的自动驾驶车辆经验记录，展示了早期自动驾驶车辆 (AV) 的节能潜力。基于车辆特定功率的微观能耗模型用于估计测试自动驾驶汽车（包括轻型汽车和重型卡车）的能源效率。总体而言，自动驾驶汽车并不一定节能，很大程度上取决于驾驶场景，例如交通状况和道路类型。在高速公路上，自动驾驶（AD）由于与人类驾驶（HD）相似的特性，节能效果并不显着（10%以内）。在车速较低且周围交通干预较多的城市道路上，AD 能够实现更高的能源效率，因为它能够更平稳地加速高达 60%；然而，频繁的人员脱离会产生 8%–40% 的额外能源消耗，尤其是在城市主干道上。我们进一步评估了行程平均速度和 AD 比率对能源效率的边际效应，说明了不同驾驶场景和功率类型的不同关系。研究建议采用更温和的AD加速来提高节能效果。然而，应用AD很难降低早期AV的能量；相反，保持混合交通流适当速度的控制方法有助于降低总能耗。