  This paper presents a novel energy-efficient motion planning algorithm for
Connected Autonomous Vehicles (CAVs) on urban roads. The approach consists of
two components: a decision-making algorithm and an optimization-based
trajectory planner. The decision-making algorithm leverages Signal Phase and
Timing (SPaT) information from connected traffic lights to select a lane with
the aim of reducing energy consumption. The algorithm is based on a heuristic
rule which is learned from human driving data. The optimization-based
trajectory planner generates a safe, smooth, and energy-efficient trajectory
toward the selected lane. The proposed strategy is experimentally evaluated in
a Vehicle-in-the-Loop (VIL) setting, where a real test vehicle receives SPaT
information from both actual and virtual traffic lights and autonomously drives
on a testing site, while the surrounding vehicles are simulated. The results
demonstrate that the use of SPaT information in autonomous driving leads to
improved energy efficiency, with the proposed strategy saving 37.1% energy
consumption compared to a lane-keeping algorithm.
