Minimizing the Disruption of Traffic Flow of Automated Vehicles During Lane Changes

Vehicles that are becoming more highly automated are revolutionizing the world's transportation systems for their promise of increased safety and efficiency. The advantage of vehicles incorporating automation is that they do not suffer from the same limitations as human drivers, such as being distracted or impaired. In order to realize the potential of these vehicles, which operate in highly dynamic environments, online techniques are needed. This paper presents such an algorithm to minimize the disruption of traffic flow by optimizing for the number of safe lane changes, thereby increasing throughput and reducing congestion. The proposed algorithm is distributed in nature and makes use of vehicle-to-vehicle and/or vehicle-to-infrastructure communication technologies to judiciously make local lane-change decisions while guaranteeing that no collisions will occur. In contrast to existing work, the proposed technique requires no assumption on the number of lanes, nor on the dynamic attributes of the vehicles such as velocity and acceleration. Simulation results show that the proposed algorithm is both efficient and effective in maximizing the number of lane changes on a given stretch of a highway.

I. Introduction

TRAFFIC congestion has become a major challenge for transportation professionals and roadway users across the world. As more of the world becomes more mobile, congestion during peak hours results in wasted time for billions of people around the globe. The effects of congestion delays on the individual are mostly negative: there is a reduction of air quality due to vehicle idling and drivers' quality of life are affected by having a large amount of nonproductive time, which results in reduced time with family and friends, as well as economic losses due to nonproductivity. Congestion also has a negative impact on safety, as it causes drivers to make increased decisions during stop and go traffic.