Longitudinal Safety Analysis For Heterogeneous Platoon Of Automated And Human Vehicles

TL;DR

This study evaluates the safety of mixed autonomous and human-driven vehicle platoons using simulation, comparing four collision avoidance algorithms across various market penetration rates and platoon configurations.

Contribution

It introduces a simulation platform to analyze heterogeneous platoon safety and compares multiple collision avoidance algorithms under different market conditions.

Findings

Collision avoidance algorithms vary in effectiveness at different penetration rates.

Higher market penetration of autonomous vehicles generally improves platoon safety.

Crash severity depends on the location of autonomous vehicles within the platoon.

Abstract

With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put their intelligent cars into road test. But in the near future, we will face a heterogeneous traffic with both intelligent connected vehicles and human vehicles. In this paper, we investigated the impacts of four collision avoidance algorithms under different intelligent connected vehicles market penetration rate. A customized simulation platform is built, in which a platoon can be initiated with many key parameters. For every short time interval, the dynamics of vehicles are updated and input in a kinematics model. If a collision occurs, the energy loss is calculated to represent the crash severity. Four collision avoidance algorithms are chosen and compared in terms of the crash rate and severity at different market penetration rate and different locations of the platoon. The results generate interesting debates on the issues of heterogeneous platoon safety.