Paving the Way for Evaluation of Connected and Autonomous Vehicles in Buses-Preliminary Analysis

Daiquan Xiao,Xuecai Xu,Shengyang Kang

doi:10.1109/access.2019.2963457

Abstract

This study intended to provide potential safety consideration that will pave the way for evaluation of connected and autonomous vehicles (CAV) in public buses. The geo-localized crash data of Las Vegas metropolitan area from 2014 to 2017 were collected, involving 27 arterials with 466 bus crash samples, and Chi-square Automatic Interaction Detection (CHAID) decision tree model was proposed to examine the effect of CAV technologies in bus crash severity so that the drivers' factors can be determined and controlled if CAV technologies were employed. Results suggest that contributory predictors of crash severity outcomes are from driver's action of vehicles with main responsibility (including going straight, making U-turn and passing other vehicles/racing), and crash type (angle and rear-end). If these factors are controlled by CAV technologies, it is suggested that severe crashes involving buses could be reduced significantly. The findings provide useful insights for CAV companies and policy makers to improve the driving state and traffic safety in public buses.

Highlights

In recent years, Connected & Autonomous Vehicle (CAV) technologies have been the hits of artificial intelligence area and the whole automotive industry, driven by billions of business opportunities and markets
The diffusion extent of CAVs will affect the crash frequency of large vehicles, and transportation policymakers are responsible for facilitating the employment of such advanced technologies, which is the motivation of this study
From 2014 to 2017, the selected arterials were involved in 35,130 crashes in Las Vegas Metropolitan area, and buses were found to have contributed to 466 of these crashes, leading to 246 property damage only (PDO), 215 injury and 5 fatality outcomes

Summary

Introduction

In recent years, Connected & Autonomous Vehicle (CAV) technologies have been the hits of artificial intelligence area and the whole automotive industry, driven by billions of business opportunities and markets. Connected vehicle (CV) technologies focus on vehicle-to-vehicle (V2V) communications by synchronizing the movements of adjacent vehicles, while autonomous vehicle (AV) technologies can replace human drivers with a series of decision-making process for a varying array of driving tasks. [3] while reducing driver fatigue, and improving drivers’ health and wellness [4]–[6]. Benefits rendered from such technologies are obvious, and how to apply these technologies into large vehicles operation is critical so that the injury by large vehicles will decline as drivers shift to autonomous driving. The diffusion extent of CAVs will affect the crash frequency of large vehicles, and transportation policymakers are responsible for facilitating the employment of such advanced technologies, which is the motivation of this study

Objectives

Results

Conclusion