Analysis of Driver Behavior and Intervehicular Collision: A Data-Based Traffic Modeling and Simulation Approach

Lu Zhao,Nadia Haddadou,Nadir Farhi,Zoi Christoforou,Tiziana Campisi

doi:10.1155/2022/1068311

Abstract

The emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. The numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers. We then replicate car collision occurrences by varying the percentages of these three driver profiles in the simulated environment, allowing us to establish a relationship between driver profiles and car collision occurrences. We also investigate the severity of car collisions and classify them with respect to the driver profiles of the cars involved in the collisions. Our approach of replicating car collision occurrences in numerical simulations will facilitate the testing and validation of ICVs in the future, especially regarding the testing of ICV functionalities in dealing with traffic accidents.

Highlights

With the development of information and telecommunication technologies, along with the rapid growth of new energy vehicles (NEVs), intelligent connected vehicles (ICVs) have become an increasingly active research topic
We propose a novel approach for car collision generation in numerical simulation by varying the percentages of different driver profiles in the traffic, aiming at establishing a relationship between driving profiles and car collision occurrences. e profiles are extracted from the Next Generation Simulation (NGSIM) database and integrated in the traffic simulator SUMO [4, 27, 28] using the calibrated intelligent driver model (IDM), which is one of the most human-like car-following models [29, 30]
In Subsection 6.2, we focus on the analysis of the car collisions obtained in the simulations of experiment 1 (E1)

Summary

Introduction

With the development of information and telecommunication technologies, along with the rapid growth of new energy vehicles (NEVs), intelligent connected vehicles (ICVs) have become an increasingly active research topic. ICVs are expected to reshape future mobility and contribute to mitigating road traffic congestion and safety problems [1]. A major challenge of ICVs is communicating with other vehicles and accurately recognizing the patterns of human driving behavior in mixed traffic [2]. To ensure the security of ICVs, they need to be tested by driving hundreds of millions of miles and need to be failure-free. Performing physical tests of ICVs is time consuming but can sometimes be dangerous as well. As an alternative to physical tests, traffic numerical simulation platforms can be used to create realistic traffic situations. Traffic accidents can be generated in traffic simulations in order to test the functionality of ICVs in dealing with traffic accidents

Methods

Results

Conclusion