An optimization-free approximation Framework for Connected and Automated Vehicles Eco-Trajectory Planning Under limited computing capacity

• Published in: Transportation research. Part C, Emerging technologies Vol. 171; p. 104949
• Main Authors: Lei, Yuan-Zheng, Cheng, Yao, Yang, Xianfeng Terry
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Connected and automated vehicles (CAV); Eco-driving; Eco-trajectory planning; Optimization-free approximation (OFA); Optimization-free approximation (OFA); Connected and automated vehicles (CAV); Eco-driving; Eco-trajectory planning
• ISSN: 0968-090X
• DOI: 10.1016/j.trc.2024.104949

The trajectory planning problem (TPP) has become increasingly crucial in the research of next-generation transportation systems, but it presents challenges due to the non-linearity its constraints. One specific case within TPP, namely the Eco-trajectory Planning Problem (EPP), poses even greater computational difficulties due to its nonlinear, high-order, and non-convex objective function. This paper proposes an optimization-free framework to address the eco-trajectory planning problem of connected and automated vehicles (CAVs) under a limited computing capacity scenario. The framework consists of an offline module and an online module. In the offline module, an optimal eco-trajectory batch is constructed by solving a sequence of simplified optimization problems to minimize fuel consumption, considering various initial and terminal system states. Each candidate trajectory in the batch yields the lowest fuel consumption subject to a specific travel time from the vehicle entry to the departure from the intersection. In the online module, an approximation framework is proposed, which greatly improves the computational efficiency of planning and only suffers from a limited extent of optimality losses through a batch-based selection process because optimization and calculation are pre-computed in the offline module. Numerical tests are presented and discussed to evaluate the computational quality and efficiency of the optimization-free approximation framework under a mixed-traffic flow environment that incorporates human-driving vehicles (HDV) and connected and automated vehicles (CAVs) with different market penetration rates (MPR) and the stochasticity of HDVs. •Optimization-free framework introduced for eco-trajectory planning of CAVs.•Offline and online modules improve computational efficiency.•Batch-based trajectory selection pre-computed to minimize fuel consumption.•Approximation framework handles emergencies and prediction errors.•Tested in mixed-traffic environments under different penetration rates.