都市交通におけるコネクテッドEVのためのエネルギー認識型車線計画：設計と車両インザループ検証
Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation
V2I通信を活用し、縦方向の速度制御と横方向の車線変更を統合的に最適化するエネルギー認識型運動計画フレームワークを提案し、車両インザループ実験で人間ドライバー比最大24%のエネルギー削減を実証した。
著者: Hansung Kim, Eric Yongkeun Choi, Eunhyek Joa, Hotae Lee, Linda Lim, Scott Moura, Francesco Borrelli
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24 percent compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy.
