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

TL;DR

This paper introduces an optimization-free framework for eco-trajectory planning of connected and automated vehicles, significantly reducing computational complexity while maintaining near-optimal fuel efficiency in mixed traffic scenarios.

Contribution

It presents a novel offline-online modular framework that simplifies trajectory planning without heavy optimization, enabling real-time application in CAV systems.

Findings

The framework achieves high computational efficiency in mixed traffic environments.

It maintains near-optimal fuel consumption with limited optimality loss.

The online algorithms effectively handle emergencies and prediction errors.

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).