Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation

2025-03-29

AI Summary

Existing eco-driving strategies for urban heterogeneous traffic and signalized intersections are limited to single-lane longitudinal speed optimization and neglect the energy impact of lateral lane changes. To address this, this paper proposes an integrated motion planning framework for connected electric vehicles (CEVs) that jointly optimizes longitudinal velocity profiles and lateral lane-changing decisions. It is the first to co-optimize speed trajectories and lane-change timing in multi-lane scenarios, introducing a graph-based long-horizon energy estimation method. The framework integrates V2I communication, short-horizon constrained optimal control, and data-driven battery energy consumption modeling. Experimental validation is conducted via vehicle-in-the-loop (VIL) testing. Results demonstrate up to 24% reduction in motion-related energy consumption compared to human driving—significantly outperforming conventional single-lane eco-driving paradigms. This work establishes the critical role of infrastructure-coordinated, multi-dimensional decision-making in enhancing energy efficiency for autonomous electric vehicles in urban environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Optimizes lane changes and speed for electric vehicles
• Reduces energy use in urban traffic with V2I communication
• Improves efficiency via connectivity and automated planning

Innovation

• Joint optimization of speed and lane changes
• Graph-based long-term energy cost estimation
• Vehicle-in-the-loop validation for energy savings