Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and data-driven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec.

翻译：车辆能耗估计旨在预测给定行程开始前所需的总能量，这对行程规划和交通可持续性具有重要意义。现有方法主要关注从典型行程中提取统计显著因素以提高车辆能耗估计精度。然而，由于不同出行情境下的个性化驾驶行为，每辆车的能耗可能存在显著差异。为此，本文提出一种偏好感知的元优化框架Meta-Pec，用于个性化车辆能耗估计。具体而言，我们首先设计一个时空行为学习模块，以捕捉历史行程中隐藏的潜在驾驶员偏好。进一步地，基于对驾驶员偏好的记忆，我们提出一个基于选择的驾驶行为预测模块，用于推断特定驾驶员在给定路线上的驾驶模式，从而为车辆能耗估计提供额外依据和监督信号。此外，我们提出一种驾驶员特定的元优化方案，通过全局学习和共享可迁移知识实现模型的快速适应。在两个真实世界数据集上的大量实验表明，我们的框架相较于十个数值和基于数据驱动的机器学习基线具有优越性。源代码可在https://github.com/usail-hkust/Meta-Pec获取。