Eco-driving Accounting for Interactive Cut-in Vehicles

TL;DR

This paper introduces an eco-driving approach for automated vehicles that considers neighboring cut-in vehicles, using a leader-follower game and model-predictive control to enhance energy efficiency in complex traffic scenarios.

Contribution

It presents a novel eco-driving design that accounts for cut-in vehicles using a leader-follower game and model-predictive control, improving energy efficiency in such interactions.

Findings

Leader-follower game effectively models cut-in vehicle interactions.

Proposed method improves energy efficiency in cut-in scenarios.

Outperforms baseline eco-driving designs that ignore neighboring vehicles.

Abstract

Automated vehicles can gather information about surrounding traffic and plan safe and energy-efficient driving behavior, which is known as eco-driving. Conventional eco-driving designs only consider preceding vehicles in the same lane as the ego vehicle. In heavy traffic, however, vehicles in adjacent lanes may cut into the ego vehicle's lane, influencing the ego vehicle's eco-driving behavior and compromising the energy-saving performance. Therefore, in this paper, we propose an eco-driving design that accounts for neighbor vehicles that have cut-in intentions. Specifically, we integrate a leader-follower game to predict the interaction between the ego and the cut-in vehicles and a model-predictive controller for planning energy-efficient behavior for the automated ego vehicle. We show that the leader-follower game model can reasonably represent the interactive motion between the ego vehicle and the cut-in vehicle. More importantly, we show that the proposed design can predict and react to neighbor vehicles' cut-in behaviors properly, leading to improved energy efficiency in cut-in scenarios compared to baseline designs that consider preceding vehicles only.