Distributed Eco-Driving Algorithm of Vehicle Platoon Using Traffic Light and Road Slope Information

TL;DR

This paper presents a two-layer eco-driving control algorithm for vehicle platoons that optimizes long-term motion planning using traffic light and road slope data, improving fuel efficiency and traffic flow.

Contribution

It introduces a novel two-layer control architecture with reachability-based long-term planning and real-time adaptation for eco-driving of vehicle platoons.

Findings

Enhanced fuel efficiency demonstrated in simulations

Effective long-term planning using traffic light and slope data

Real-time adaptation maintains safe inter-vehicle distances

Abstract

This paper investigates the problem of ecological driving (eco-driving) of vehicle platoons. To reduce the probability of the platoon avoiding red lights and increase fuel efficiency, a two-layer control architecture is proposed. The first layer is in charge of optimizing the leader's long-term motion profile using the traffic light and road slope information. The long-term planning model is defined based on the reachability analysis of the platoon to the green light windows. An event-triggered mechanism is proposed to operate the long-term planning model. The second layer is the short-term adaptation, in which the leader attempts to follow the planning motion profile in real time, while the follower keeps track of the nearest preceding vehicle and the leader, to preserve the desired inter-vehicular distances. A Newton's method-based algorithm is implemented to effectively solve both the long-term planning and short-term adaptation problems. The effectiveness of the proposed formulation and algorithm is illustrated by the simulations.