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Vehicle Safety and Crash Avoidance

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 17097

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Special Issue Editors

Prof. Dr. Xinguo Jiang

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Guest Editor

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Interests: safety modeling; safety behavior analysis; surrogate traffic safty analysis; traffic safety evaluation

Dr. Chuanyun Fu

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Guest Editor

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: real-time safety analysis; autonomous driving safety evaluation; traffic behavior analysis and modeling; and advanced statistical modeling

Dr. Chuan Xu

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Guest Editor

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Interests: urban road and freeway safety analysis; truck safety; vulnerable roader user safety; using connected and autonomous vehicle data to enhance safety; safety performance quantification

Special Issue Information

Dear Colleagues,

Traffic crashes remain a significant societal issue today, and determining how to reduce the frequency and severity of such crashes are essential questions for both academia and industry. Expanding the availability of vehicle systems and features that help to avoid crashes through advancements in vehicle safety technology is a robust approach to responding to the safety challenge.

Several cutting-edge technologies have been introduced to improve vehicle safety, including 5G, big data, artificial intelligence, cloud computing, edge computing, advanced sensing, etc. Based on these technologies, autonomous driving, connected vehicles, and advanced driver assistance systems (such as forward collision warning, lane departure warning, crash imminent braking, dynamic braking support, pedestrian automatic emergency braking, etc.) facilitate the development of vehicle safety and crash avoidance.

In this context, this Special Issue titled “Vehicle Safety and Crash Avoidance” is proposed to promote ideas and introduce state-of-the-art methods and technologies, as well as the potential applications that they can be used for. This collection welcomes any recent studies pertaining to cutting-edge technologies for vehicle safety and crash avoidance.

The topics of interest for this Special Issue include but are not limited to the following:

Proactive road safety analysis;
Real-time safety analysis;
Real-time safety route planning;
Autonomous driving safety evaluation;
Emerging methods and technologies for ecodriving;
Dispatch, operation, and management for new energy vehicles;
Freeway safety in mixed traffic conditions (human driving and autonomous vehicles);
The safety of vulnerable road users in the urban environment;
Motorized and non-motorized mixed traffic modeling method;
Autonomous vehicle mixed traffic simulation and safety analysis;
Safety traffic operations for the mixed traffic of human-driven vehicles and CAVs;
AI-empowered traffic safety analytics using vehicle trajectory data;
AI-empowered CAV management and operations;
Novel policies for ensuring CAV safety;
Future perspectives of CAV safety;
Vehicle crash avoidance technology;
Advanced driving assistance system evaluation.

Prof. Dr. Xinguo Jiang
Dr. Chuanyun Fu
Dr. Chuan Xu
Guest Editors

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Keywords

traffic safety
highway safety
crash risk
traffic conflict
risky driving behavior
autonomous driving human driving
autonomous vehicle
connected vehicle
connected and automated vehicle
motorized vehicle
non-motorized vehicle
crash avoidance
vulnerable road user
advanced driver assistance systems

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Published Papers (11 papers)

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Research

12 pages, 1373 KiB

Open AccessArticle

Exploration of the Characteristics of Elderly-Driver-Involved Single-Vehicle Hit-Fixed-Object Crashes in Pennsylvania, USA

by Xuerui Hou, Zihao Zhang, Xue Su and Chenhui Liu

Appl. Sci. 2024, 14(19), 8625; https://doi.org/10.3390/app14198625 - 25 Sep 2024

Viewed by 868

Abstract

With the acceleration of population aging, the elderly driving safety issue is increasingly prominent. Method: With the crash data of Pennsylvania from 2010 to 2019, this study exclusively discusses features of single-vehicle hit-fixed-object crashes (SVHFOCs), one of the most common and deadliest crash types for elderly drivers. Results: Firstly, we demonstrate that elderly drivers are much more likely to be injured and killed than young drivers in SVHFOCs by checking crash consequences. The descriptive analysis indicates that elderly drivers have very different crash features from young drivers. They are found to drive with more caution in many aspects, such as more low-speed local travels, fewer illegal behaviors, fewer nighttime travels, etc. Then, a logistic regression model is built to find the factors significantly influencing the severity of SVHFOCs from driver, vehicle, roadway, and environment. The estimation results indicate that female sex, not wearing a seatbelt, DUI, rural area, and SUV involvement tend to be associated with more severe SVHFOCs. Additionally, illumination, weather, and road type could also significantly affect crash severity. Especially, SVHFOCs in adverse weather, in dark conditions, and at intersections are found to be less severe, which implies that elderly drivers might drive more carefully in complex environments. Practical Applications: These findings are expected to provide new insights for agencies in formulating customized measures to prevent elderly drivers from being involved in SVHFOCs. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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17 pages, 6284 KiB

Open AccessArticle

Estimating Urban Traffic Safety and Analyzing Spatial Patterns through the Integration of City-Wide Near-Miss Data: A New York City Case Study

by Chuan Xu, Jingqin Gao, Fan Zuo and Kaan Ozbay

Appl. Sci. 2024, 14(14), 6378; https://doi.org/10.3390/app14146378 - 22 Jul 2024

Viewed by 1041

Abstract

City-wide near-miss data can be beneficial for traffic safety estimation. In this study, we evaluate urban traffic safety and examine spatial patterns by incorporating city-wide near-miss data (59,277 near-misses). Our methodology employs a grid-based method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran’s I and local Moran’s I. The study findings reveal that near-misses have the strongest correlation with observed crash frequency among all the variables studied. Interestingly, the ratio of near-misses to crashes is roughly estimated to be 1957:1, providing a potentially useful benchmark for urban areas. For other variables, an increased number of intersections and bus stops, along with a greater road length, contribute to a higher crash frequency. Conversely, residential and open-space land use rates show a negative correlation with crash frequency. Through spatial analysis, potential risk hotspots including roads linking bridges and tunnels, and avenues bustling with pedestrian activity, are highlighted. The study also identified negative local spatial correlations in crash frequencies, suggesting significant safety risk variations within relatively short distances. By mapping the differences between observed and predicted crash frequencies, we identified specific grid areas with unexpectedly high or low crash frequencies. These findings highlight the crucial role of near-miss data in urban traffic safety policy and planning, particularly relevant with the imminent rise of autonomous and connected vehicles. By integrating near-miss data into safety estimations, we can develop a more comprehensive understanding of traffic safety and, thus, more effectively address urban traffic risks. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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28 pages, 32347 KiB

Open AccessArticle

Performance—Based Route Selection for Mountainous Highways: A Numerical Approach to Addressing Safety, Hydrological, and Geological Aspects

by Dalia Said, Ahmed Foda, Ahmed Abdelhalim and Mustafa Elkhedr

Appl. Sci. 2024, 14(13), 5844; https://doi.org/10.3390/app14135844 - 4 Jul 2024

Viewed by 840

Abstract

This study presents an innovative methodology for Highway Route Selection (HRS), specifically tailored for mountainous terrains. The approach focuses on selecting the most suitable route for road alignment while prioritizing road safety and hydrological and geological considerations. Through systematic analysis, the methodology evaluates alternative road alignments by examining various risk factors related to geometric design, hydrological, and geological impacts. Utilizing Fault-Tree Analysis (FTA), 14 key design factors related to geometric design and environmental factors are identified. The results demonstrate the effectiveness of the methodology in selecting road alignments that enhance safety and mitigate environmental risks. A case study is presented where a 90-km segment of a road in Egypt’s Golden Triangle Project, characterized by challenging terrain and diverse geological features, is examined. Through detailed analysis, the study identifies critical design factors to enhance road safety and minimize environmental impact. The methodology’s comprehensive approach offers insights into road design, providing a quantitative framework for decision-making and mitigation strategies. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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22 pages, 1856 KiB

Open AccessArticle

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

by Siwei Fang, Guiqi Zhang, Yufeng Li and Jiangtao Li

Appl. Sci. 2024, 14(7), 2805; https://doi.org/10.3390/app14072805 - 27 Mar 2024

Cited by 1 | Viewed by 1201

Abstract

The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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24 pages, 14185 KiB

Open AccessArticle

A New Surrogate Safety Measure Considering Temporal–Spatial Proximity and Severity of Potential Collisions

by Shuning Tang, Yichen Lu, Yankun Liao, Kai Cheng and Yajie Zou

Appl. Sci. 2024, 14(7), 2711; https://doi.org/10.3390/app14072711 - 23 Mar 2024

Cited by 1 | Viewed by 1246

Abstract

Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal–spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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24 pages, 5969 KiB

Open AccessArticle

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

by Xiaomeng Wang, Xuanzong Wei and Xuesong Wang

Appl. Sci. 2024, 14(6), 2369; https://doi.org/10.3390/app14062369 - 11 Mar 2024

Viewed by 987

Abstract

The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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37 pages, 5728 KiB

Open AccessArticle

Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios

by Fumin Zou, Chenxi Xia, Feng Guo, Xinjian Cai, Qiqin Cai, Guanghao Luo and Ting Ye

Appl. Sci. 2023, 13(23), 12899; https://doi.org/10.3390/app132312899 - 1 Dec 2023

Viewed by 1159

Abstract

Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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11 pages, 3391 KiB

Open AccessArticle

Calculation of Dangerous Driving Index for Two-Wheeled Vehicles Using the Analytic Hierarchy Process

by Suyun Lee, Dongbeom Kim and Chulmin Jun

Appl. Sci. 2023, 13(22), 12377; https://doi.org/10.3390/app132212377 - 16 Nov 2023

Viewed by 1245

Abstract

Given the high incidence of traffic accidents and fatalities on two-wheeled vehicles, there is a growing need for safety management. However, studies on evaluating two-wheeled vehicle driving in a quantitative and comprehensive form are insufficient. In this study, 11 items were defined for the first step to evaluate two-wheeled vehicle driving: signal violation, central line violation, helmet violation, pedestrian close driving, sidewalk driving, reverse lane driving, speed violation, rapid acceleration, rapid deceleration, rapid turn, and rapid lane change. The items were classified into three categories (traffic violation, pedestrian threat, and reckless driving), and their weights were derived using the AHP technique. For rapid acceleration, rapid deceleration, rapid turn, and rapid lane change, a high-performance driving simulator was used to establish risk criteria and calculate the weight based on the degree of risk. The calculated weight of each item indicates its importance in evaluating two-wheeled vehicle driving, with helmet violation (0.158), speed violation (0.124), and pedestrian close driving (0.122) having the highest weights. Finally, the dangerous driving index for two-wheeled vehicles was calculated by the weights of each evaluation item and applied to the driving trajectory data. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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19 pages, 6817 KiB

Open AccessArticle

Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountainous Expressways

by Bo Zhang, Jingrong Bai, Zhiwen Yin, Ao Zhou and Jue Li

Appl. Sci. 2023, 13(18), 10186; https://doi.org/10.3390/app131810186 - 11 Sep 2023

Cited by 4 | Viewed by 1429

Abstract

Mountainous expressways with bridge-tunnel groups are characterized by complex environments and high driving risks, making them crucial sections for highway safety. This study applied eye-tracking techniques to evaluate driving safety and comfort in bridge-tunnel groups. Drivers’ pupil diameter and fixation point distribution were measured in real vehicle tests. The influence of tunnel length, adjacent tunnel spacing, and natural lighting on drivers’ pupil diameters were compared and analyzed. The maximum transient velocity of pupil area was introduced to describe the drivers’ visual load and driving comfort. The results indicate that the driving workload reaches its maximum in the first tunnel in bridge-tunnel groups and is positively correlated with the tunnel length in other sections. Excessive or insufficient distance between adjacent tunnels is detrimental to driving comfort. The driving workload is higher at night compared to during the day. Moreover, the greater tunnel length in bridge-tunnel groups and the larger number of tunnels, suggest a higher driving workload for drivers. Above all, strengthening the design and management of bridge-tunnel groups in mountainous expressways is necessary. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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17 pages, 966 KiB

Open AccessArticle

Modeling Driver’s Real-Time Confidence in Autonomous Vehicles

by Jiayi Lu, Shichun Yang, Yuan Ma, Runwu Shi, Zhaoxia Peng, Zhaowen Pang, Yuyi Chen, Xinjie Feng, Rui Wang, Rui Cao, Yibing Liu, Qiuhong Wang and Yaoguang Cao

Appl. Sci. 2023, 13(7), 4099; https://doi.org/10.3390/app13074099 - 23 Mar 2023

Cited by 4 | Viewed by 2357

Abstract

Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called “the Virtual Confidence (VC)” by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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14 pages, 1168 KiB

Open AccessArticle

The Impact of Gamifications and Serious Games on Driving under Unfamiliar Traffic Regulations

by Hasan Alyamani, Nesreen Alharbi, Amjad Roboey and Manolya Kavakli

Appl. Sci. 2023, 13(5), 3262; https://doi.org/10.3390/app13053262 - 3 Mar 2023

Cited by 4 | Viewed by 3157

Abstract

Drivers face many challenges when driving under unfamiliar traffic regulations, which may lead to a reduction in road safety. The need to adjust to different traffic rules could be a major factor toward a safer drive. Gamification is a promising way to enhance the user engagement in non-game tasks. In this paper, we hypothesize that gamification can improve driving performance and minimize the number of driving errors when driving under unfamiliar traffic regulations and thus enhance road safety. A game was designed to provide gamification elements in a simulated driving environment with unfamiliar traffic regulations where the players were motivated to reach the target with no errors. In the experiments, 14 participants who were not familiar with the designed traffic regulations were asked to drive a car simulator in two scenarios. The first scenario had no gamification elements, whereas the second one included gamification elements. The results indicated that gamification significantly helped the participants to drive in the correct traffic flow with the proper use of vehicle configuration. Our findings show that gamified simulation is a reasonable method to adjust the required driving performance and behavior to safely drive under unfamiliar traffic regulations. Full article

(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)

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