An optimization-free approximation Framework for Connected and Automated Vehicles Eco-Trajectory Planning Under limited computing capacity
Yuanzheng Lei, Yao Cheng, Xianfeng Terry Yang
TL;DR
The paper tackles the computational intractability of eco-trajectory planning for CAVs by introducing an optimization-free approximation (OFA) framework that pre-computes an optimal eco-trajectory batch offline and realigns it online through translation, truncation, and smoothing to satisfy time-sensitive constraints. The offline module uses a discrete-time model and a VT-micro fuel-emission formulation to generate a set of fuel-efficient trajectories across possible travel times; the online module then selects and adapts these trajectories in real time under HDV stochasticity and signal constraints. Key contributions include the offline generation of a comprehensive trajectory batch, a translation-based online validation mechanism, and substantial computational gains (online times under 1 ms) with notable fuel savings as CAV market penetration grows. The framework demonstrates practical potential for real-time eco-driving with limited computing capacity in mixed-traffic environments, while future work could extend to lane-changing dynamics and broader driving scenarios.
Abstract
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 of 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) in the straight-driving 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, dynamic trajectory planning algorithms based on different scenarios are provided. Both algorithms greatly improve the computational efficiency of planning and only suffer from a limited extent of optimality losses through a batch-based selection process because optimization and calculation are pre-computed in the offline module. The latter algorithm can also handle possible emergencies and prediction errors. 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 (CAV) with different market penetration rates (MPR).