Vehicle Safety and Crash Avoidance

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 June 2024 | Viewed by 6110

Special Issue Editors

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

Manuscript Submission Information

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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

Published Papers (5 papers)

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Research

37 pages, 5728 KiB  
Article
Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios
Appl. Sci. 2023, 13(23), 12899; https://doi.org/10.3390/app132312899 - 01 Dec 2023
Viewed by 530
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 [...] Read more.
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  
Article
Calculation of Dangerous Driving Index for Two-Wheeled Vehicles Using the Analytic Hierarchy Process
Appl. Sci. 2023, 13(22), 12377; https://doi.org/10.3390/app132212377 - 16 Nov 2023
Viewed by 602
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 [...] Read more.
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  
Article
Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountainous Expressways
Appl. Sci. 2023, 13(18), 10186; https://doi.org/10.3390/app131810186 - 11 Sep 2023
Cited by 1 | Viewed by 682
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 [...] Read more.
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  
Article
Modeling Driver’s Real-Time Confidence in Autonomous Vehicles
Appl. Sci. 2023, 13(7), 4099; https://doi.org/10.3390/app13074099 - 23 Mar 2023
Cited by 2 | Viewed by 1550
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 [...] Read more.
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  
Article
The Impact of Gamifications and Serious Games on Driving under Unfamiliar Traffic Regulations
Appl. Sci. 2023, 13(5), 3262; https://doi.org/10.3390/app13053262 - 03 Mar 2023
Cited by 1 | Viewed by 1805
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 [...] Read more.
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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