A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

• URL: http://arxiv.org/abs/2306.14421v1
• Date: Mon, 26 Jun 2023 05:03:24 GMT
• Title: A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation
• Abstract summary: Vehicle Energy Consumption estimation aims to predict the total energy required for a given trip before it starts. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. This paper proposes a preference-aware metaoptimization framework Meta-Pec for personalized vehicle energy consumption estimation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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