Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

Ren, Ruiming; Li, Hang; Han, Tianfang; Tian, Chi; Zhang, Cong; Zhang, Jiansong; Proctor, Robert W.; Chen, Yunfeng; Feng, Yiheng

doi:10.1016/j.aap.2023.107021

Cited by 13 publications

(3 citation statements)

References 109 publications

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“…While co-simulation platforms have been used to study single autonomous vehicles and pedestrian-vehicle interactions, there is a lack of research on multi-player online driving [64,65]. Existing platforms often focus on simulating driving for a single vehicle and pedestrian simulation platforms lack support from traffic simulation software.…”

Section: Co-simulation Platformsmentioning

confidence: 99%

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

Shi

2023

Systems

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This paper presents the system development of a co-simulation platform aimed at studying driving behavior with multiple participants. The objective of this study was to create an immersive and interactive environment where different driving scenarios could be simulated and driver behavior could be recorded and analyzed. The platform integrated the Unity game engine with the VISSIM microscopic traffic simulator to create a hybrid simulation environment that combined the advantages of both tools. A virtual reality massive multiplayer online (VRMMO) module was developed to capture the interactions of the participants during the simulation experiments. The external control devices of this co-simulation platform were calibrated using the empirical data of a Controller Area Network (CAN-BUS) from actual driving behaviors. The main contributions of this study are the demonstration of the Unity–VISSIM co-simulation platform in simulating interactive driver behavior and the potential for its use in various research areas, such as intelligent transportation systems, human factors, driving education, and traffic safety analyses. The platform could be a valuable tool for evaluating the effectiveness of collective intelligence countermeasures in improving traffic systems, with relatively lower costs and risks.

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Section: Co-simulation Platformsmentioning

confidence: 99%

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

Shi

2023

Systems

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“…Other significant factors influencing traffic accident results include the nature of the road where the accident occurred and the collision structure. However, this knowledge is restricted due to the absence of extensive research studies comparing these different communities [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

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“…They also can increase employment and other activities for people who cannot drive. During the early stages of use, CAVs will have to share the roadways with human-driven vehicles (HDVs), necessitating effective communication among CAVs and HDVs about their intentions and future actions (Feng et al, 2023; Hancock et al, 2020; Ren et al, 2023). We previously reported results of a survey of drivers that assessed their knowledge of CAVs and thoughts about their deployment (Hung et al, 2022).…”

mentioning

confidence: 99%

Experts’ Evaluations and Opinions Concerning Deployment of Connected and Autonomous Vehicles

Han,

Hung,

Bell

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Connected and autonomous vehicles (CAVs) afford the possibility of improving the transportation system by reducing traffic congestion and accidents. They also can increase employment and shopping opportunities for persons who cannot drive. During the early stages of use, CAVs will have to share the roadways with human-driven vehicles (HDVs), necessitating effective communication among CAVs and HDVs about their intentions and future actions. We previously reported results of a survey of drivers that assessed their knowledge of CAVs and thoughts about their deployment. In the present study, we describe the evaluations and opinions of CAV experts from academia, industry, and government obtained from in-depth interviews.

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Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways

Cited by 13 publications

References 109 publications

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Experts’ Evaluations and Opinions Concerning Deployment of Connected and Autonomous Vehicles

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