Pub Date : 2024-10-24DOI: 10.1016/j.aap.2024.107825
Shoushuo Wang , Lei Han , Zhigang Du , Shiming He , Haoran Zheng , Liu Yang , Fangtong Jiao
In order to investigate whether retroreflective rings can enhance drivers’ perception of spatial right-of-way in freeway tunnels, this paper explores a simulation test. The characteristics of spatial right-of-way in tunnels are elucidated, and a comparative test is conducted using commonly used delineators and raised pavement markers against retroreflective rings to enhance the perception of spatial right-of-way. The test employs the perception of lateral deviation and longitudinal distance as indicators to reflect the lateral and longitudinal right-of-way. Video scenarios, incorporating different facilities and spacing, are created using 3Ds Max software following the design standards of freeway tunnels. The indicators of Stimulation of Subjectively Equal Distance (SSED), lateral deviation, and perception reaction time (PRT) are chosen to assess the effects of different facilities on drivers under varying spacing conditions. Fifty-two participants, divided into two groups of novice drivers and experienced drivers, underwent perception testing in a simulated driving environment. The results indicate that drivers exhibit the highest overestimation of longitudinal distance and the longest PRT of lateral deviation in the absence of facilities. Installing retroreflective rings with a spacing of 50–200 m significantly mitigates the overestimation of longitudinal distance, while reducing the PRT of lateral deviation. On the other hand, setting up delineators and raised pavement markers with a spacing of 6–12 m significantly reduces the PRT of lateral deviation, while there is no significant enhancement to the perception of longitudinal distance. A spacing of 200 m for retroreflective rings and 10 m for delineators and raised pavement markers in the straight section is recommended as a safer and more economical setting scheme. The combination of these facilities can enhance drivers’ safety perception of spatial right-of-way in freeway tunnels, facilitating rapid perception, correct judgment, and timely decision-making for the safe passage of vehicles.
{"title":"Can retroreflective rings enhance drivers’ safety perception of spatial right-of-way in freeway tunnels? A simulation exploration","authors":"Shoushuo Wang , Lei Han , Zhigang Du , Shiming He , Haoran Zheng , Liu Yang , Fangtong Jiao","doi":"10.1016/j.aap.2024.107825","DOIUrl":"10.1016/j.aap.2024.107825","url":null,"abstract":"<div><div>In order to investigate whether retroreflective rings can enhance drivers’ perception of spatial right-of-way in freeway tunnels, this paper explores a simulation test. The characteristics of spatial right-of-way in tunnels are elucidated, and a comparative test is conducted using commonly used delineators and raised pavement markers against retroreflective rings to enhance the perception of spatial right-of-way. The test employs the perception of lateral deviation and longitudinal distance as indicators to reflect the lateral and longitudinal right-of-way. Video scenarios, incorporating different facilities and spacing, are created using 3Ds Max software following the design standards of freeway tunnels. The indicators of Stimulation of Subjectively Equal Distance (SSED), lateral deviation, and perception reaction time (PRT) are chosen to assess the effects of different facilities on drivers under varying spacing conditions. Fifty-two participants, divided into two groups of novice drivers and experienced drivers, underwent perception testing in a simulated driving environment. The results indicate that drivers exhibit the highest overestimation of longitudinal distance and the longest PRT of lateral deviation in the absence of facilities. Installing retroreflective rings with a spacing of 50–200 m significantly mitigates the overestimation of longitudinal distance, while reducing the PRT of lateral deviation. On the other hand, setting up delineators and raised pavement markers with a spacing of 6–12 m significantly reduces the PRT of lateral deviation, while there is no significant enhancement to the perception of longitudinal distance. A spacing of 200 m for retroreflective rings and 10 m for delineators and raised pavement markers in the straight section is recommended as a safer and more economical setting scheme. The combination of these facilities can enhance drivers’ safety perception of spatial right-of-way in freeway tunnels, facilitating rapid perception, correct judgment, and timely decision-making for the safe passage of vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107825"},"PeriodicalIF":5.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.aap.2024.107815
Mahmuda Sultana Mimi , Rohit Chakraborty , Jinli Liu , Swastika Barua , Subasish Das
Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017–2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65–74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.
{"title":"Exploring patterns in older pedestrian involved crashes during nighttime","authors":"Mahmuda Sultana Mimi , Rohit Chakraborty , Jinli Liu , Swastika Barua , Subasish Das","doi":"10.1016/j.aap.2024.107815","DOIUrl":"10.1016/j.aap.2024.107815","url":null,"abstract":"<div><div>Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017–2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65–74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107815"},"PeriodicalIF":5.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21DOI: 10.1016/j.aap.2024.107807
Jordan Poon, Yiik Diew Wong
The number of accidents involving elderly pedestrians has been increasing from year to year, in spite of various road safety initiatives having been implemented. In line with Singapore’s ageing population, this presents a worrying trend. This study aims to shed light on possible contributing factors via a human factors analysis. A preliminary investigation was first conducted at traffic junctions identified to have a greater occurrence of accidents involving elderly pedestrians and motorists. This preliminary investigation looked into the efficacy of infrastructure-oriented solutions in reducing the occurrence of such accidents. It was observed that infrastructure alone was inadequate in ensuring safety of elderly pedestrians. Next, a questionnaire was administered in order to gain information regarding traits, attitudes and behaviours pertinent to traffic safety. Subsequently, structural equation modelling was used to analyse the data via exploratory, confirmatory and path analysis. This was followed by an in-depth discussion which explored the relationship between the latent constructs of traits, attitudes and behaviours, as well as social demographic variables such as age, gender and education level. It was found that poor cognitive ability and poor attitudes towards transport safety were both positively correlated with unsafe behaviour; strong psychosocial beliefs were positively correlated with poor attitudes towards transport safety, but negatively correlated with unsafe behaviour. The study concludes with recommendations to improve traffic outcomes for the elderly.
{"title":"Attitudes and behaviour of elderly in cognisance of transport safety when navigating pedestrian facilities","authors":"Jordan Poon, Yiik Diew Wong","doi":"10.1016/j.aap.2024.107807","DOIUrl":"10.1016/j.aap.2024.107807","url":null,"abstract":"<div><div>The number of accidents involving elderly pedestrians has been increasing from year to year, in spite of various road safety initiatives having been implemented. In line with Singapore’s ageing population, this presents a worrying trend. This study aims to shed light on possible contributing factors via a human factors analysis. A preliminary investigation was first conducted at traffic junctions identified to have a greater occurrence of accidents involving elderly pedestrians and motorists. This preliminary investigation looked into the efficacy of infrastructure-oriented solutions in reducing the occurrence of such accidents. It was observed that infrastructure alone was inadequate in ensuring safety of elderly pedestrians. Next, a questionnaire was administered in order to gain information regarding traits, attitudes and behaviours pertinent to traffic safety. Subsequently, structural equation modelling was used to analyse the data via exploratory, confirmatory and path analysis. This was followed by an in-depth discussion which explored the relationship between the latent constructs of traits, attitudes and behaviours, as well as social demographic variables such as age, gender and education level. It was found that poor cognitive ability and poor attitudes towards transport safety were both positively correlated with unsafe behaviour; strong psychosocial beliefs were positively correlated with poor attitudes towards transport safety, but negatively correlated with unsafe behaviour. The study concludes with recommendations to improve traffic outcomes for the elderly.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107807"},"PeriodicalIF":5.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-19DOI: 10.1016/j.aap.2024.107811
Markus Tomzig , Johanna Wörle , Sebastian Gary , Martin Baumann , Alexandra Neukum
At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.
In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.
Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.
These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.
{"title":"Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration","authors":"Markus Tomzig , Johanna Wörle , Sebastian Gary , Martin Baumann , Alexandra Neukum","doi":"10.1016/j.aap.2024.107811","DOIUrl":"10.1016/j.aap.2024.107811","url":null,"abstract":"<div><div>At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.</div><div>In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.</div><div>Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.</div><div>These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107811"},"PeriodicalIF":5.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.aap.2024.107804
Meng Li , Yan Zhang , Tao Chen , Hao Du , Kaifeng Deng
China is a major cycling nation with nearly 400 million bicycles, significantly alleviating urban traffic congestion. However, safety concerns are prominent, with approximately 35% of cyclists forming groups with family, friends, or colleagues, exerting a significant impact on the traffic system. This study focuses on group cycling, employing urban cycling experiments, GPS trajectory tracking, and eye-tracking to analyze the visual search, and cycling control of both groups and individuals. Findings reveal that group cyclists tend to focus more on companions, leading to a dispersed gaze pattern compared to individual riders who focus more on the direct path and surroundings. Group riders also exhibit shorter fixation times on traffic signs, potentially indicating decreased attention to traffic regulations. Despite similar lateral position deviation, group cyclists exhibit higher steering entropy, indicating greater variability in their steering choices. Additionally, group riders demonstrate varied passing times, suggesting a collective advantage in navigating complex traffic conditions. This study enhances our understanding of bicycles within traffic dynamics, offering valuable insights for traffic management systems.
{"title":"Group cycling in urban environments: Analyzing visual attention and riding performance for enhanced road safety","authors":"Meng Li , Yan Zhang , Tao Chen , Hao Du , Kaifeng Deng","doi":"10.1016/j.aap.2024.107804","DOIUrl":"10.1016/j.aap.2024.107804","url":null,"abstract":"<div><div>China is a major cycling nation with nearly 400 million bicycles, significantly alleviating urban traffic congestion. However, safety concerns are prominent, with approximately 35% of cyclists forming groups with family, friends, or colleagues, exerting a significant impact on the traffic system. This study focuses on group cycling, employing urban cycling experiments, GPS trajectory tracking, and eye-tracking to analyze the visual search, and cycling control of both groups and individuals. Findings reveal that group cyclists tend to focus more on companions, leading to a dispersed gaze pattern compared to individual riders who focus more on the direct path and surroundings. Group riders also exhibit shorter fixation times on traffic signs, potentially indicating decreased attention to traffic regulations. Despite similar lateral position deviation, group cyclists exhibit higher steering entropy, indicating greater variability in their steering choices. Additionally, group riders demonstrate varied passing times, suggesting a collective advantage in navigating complex traffic conditions. This study enhances our understanding of bicycles within traffic dynamics, offering valuable insights for traffic management systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107804"},"PeriodicalIF":5.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.aap.2024.107814
Rune Elvik
This paper compares actual and perceived risk of apprehension for speeding in Norway. Actual risk of apprehension was estimated by relying on data on the number of citations for speeding and the percentage of vehicles speeding when passing automatic traffic counting stations. It was defined as the number of detected violations per million kilometres driven while committing a violation. Perceived risk of apprehension was estimated as a mean annual frequency of getting detected by the police, based on survey answers given by samples of drivers surveyed in 2010, 2014 and 2024. Actual risk of apprehension was converted into a mean annual frequency of detection by relying on estimates of the mean annual driving distance. Thus, perceived mean annual frequency of detection could be compared to actual mean annual frequency of detection. Drivers were found to overestimate the risk of apprehension considerably, but the size of the overestimation declined from 2010 to 2014 and further again to 2024. In 2024, mean perceived risk of apprehension was about 2.4 times higher than actual risk of apprehension. Drivers were also found to overestimate the number of speed cameras deployed in Norway. Only a small minority of drivers had a correct perception of how the risk of apprehension for speeding varied according to the level of speeding. The decisions drivers make about speeding are based on their perceived risk of apprehension; hence it is advantageous to compliance that drivers overestimate the risk of apprehension.
{"title":"A comparison of actual and perceived risk of apprehension for speeding in Norway","authors":"Rune Elvik","doi":"10.1016/j.aap.2024.107814","DOIUrl":"10.1016/j.aap.2024.107814","url":null,"abstract":"<div><div>This paper compares actual and perceived risk of apprehension for speeding in Norway. Actual risk of apprehension was estimated by relying on data on the number of citations for speeding and the percentage of vehicles speeding when passing automatic traffic counting stations. It was defined as the number of detected violations per million kilometres driven while committing a violation. Perceived risk of apprehension was estimated as a mean annual frequency of getting detected by the police, based on survey answers given by samples of drivers surveyed in 2010, 2014 and 2024. Actual risk of apprehension was converted into a mean annual frequency of detection by relying on estimates of the mean annual driving distance. Thus, perceived mean annual frequency of detection could be compared to actual mean annual frequency of detection. Drivers were found to overestimate the risk of apprehension considerably, but the size of the overestimation declined from 2010 to 2014 and further again to 2024. In 2024, mean perceived risk of apprehension was about 2.4 times higher than actual risk of apprehension. Drivers were also found to overestimate the number of speed cameras deployed in Norway. Only a small minority of drivers had a correct perception of how the risk of apprehension for speeding varied according to the level of speeding. The decisions drivers make about speeding are based on their perceived risk of apprehension; hence it is advantageous to compliance that drivers overestimate the risk of apprehension.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107814"},"PeriodicalIF":5.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.aap.2024.107812
Yifan Sun , Rong Wang , Hui Zhang , Naikan Ding , Sara Ferreira , Xiang Shi
<div><h3>Background</h3><div>Drowsiness detection is a long-standing concern in preventing drowsiness-related accidents. Inter-individual differences seriously affect drowsiness detection accuracy. However, most existing studies neglected inter-individual differences in measurements’ calculation parameters and drowsiness thresholds. Studies without considering inter-individual differences generally used selfsame measurements and drowsiness thresholds for each participant rather than individual optimal measurements and personalized thresholds, which reduces the contribution of measurements and drowsiness detection accuracy at the individual level. Additionally, Driving Fingerprinting (DF) that represents individual traits has not been well applied in drowsiness detection.</div></div><div><h3>Methods</h3><div>We built the Individualized Drowsy driving Detection Model (IDDM) utilizing DF, extracting individual driver’s optimal drowsiness characteristics to detect drowsiness. Firstly, we conducted simulated driving experiments with 24 participants (2:1 male-to-female ratio, diverse ages and occupations including professional taxi drivers and graduate students) and collected data on their driving behavior, facial expressions, and the Karolinska Sleepiness Scale (KSS). Secondly, we employed a Two-layer Sliding Time Window (TSTW) to calculate DF measurements. Thirdly, we utilized attribution directed graphs to visualize DF, understand changes in DF with drowsiness, and analyze accident risks. Finally, we used DF matrices to build the IDDM. The IDDM utilized an improved adaptive genetic algorithm to extract the optimal drowsiness characteristics of individual drivers. These DF matrices, constituted by the optimal drowsiness characteristics of individual drivers, were used to train the IDDM based on principal component analysis and radial basis function neural networks. The TSTW strengthened the variation of DF with drowsiness, and the trained IDDM excavated the relationships between DF characteristics and drowsiness, which improved the accuracy and end-to-end timeliness of practical applications. The DF visualization displayed DF variations with drowsiness, theoretically supporting the use of DF to enhance personalized drowsiness driving detection.</div></div><div><h3>Results</h3><div>The DF visualization indicated drowsiness caused the distribution and transition probabilities of DF measurements to shift toward unsafe directions, thereby increasing the accident risk and demonstrating the rationality for utilizing DF to recognize drowsiness. The proposed IDDM achieved average accuracy, sensitivity, and specificity of 95.58 %, 96.50 %, and 94.70 %, respectively, outperforming most existing models. The trained IDDM demonstrated an average execution time of 0.0078 s and lower computational costs due to the reduction of PCA and simple RBFNN compared with models based on deep learning, and no requiring physiological data, which reduced invasiveness and enhanc
{"title":"Driving fingerprinting enhances drowsy driving detection: Tailoring to individual driver characteristics","authors":"Yifan Sun , Rong Wang , Hui Zhang , Naikan Ding , Sara Ferreira , Xiang Shi","doi":"10.1016/j.aap.2024.107812","DOIUrl":"10.1016/j.aap.2024.107812","url":null,"abstract":"<div><h3>Background</h3><div>Drowsiness detection is a long-standing concern in preventing drowsiness-related accidents. Inter-individual differences seriously affect drowsiness detection accuracy. However, most existing studies neglected inter-individual differences in measurements’ calculation parameters and drowsiness thresholds. Studies without considering inter-individual differences generally used selfsame measurements and drowsiness thresholds for each participant rather than individual optimal measurements and personalized thresholds, which reduces the contribution of measurements and drowsiness detection accuracy at the individual level. Additionally, Driving Fingerprinting (DF) that represents individual traits has not been well applied in drowsiness detection.</div></div><div><h3>Methods</h3><div>We built the Individualized Drowsy driving Detection Model (IDDM) utilizing DF, extracting individual driver’s optimal drowsiness characteristics to detect drowsiness. Firstly, we conducted simulated driving experiments with 24 participants (2:1 male-to-female ratio, diverse ages and occupations including professional taxi drivers and graduate students) and collected data on their driving behavior, facial expressions, and the Karolinska Sleepiness Scale (KSS). Secondly, we employed a Two-layer Sliding Time Window (TSTW) to calculate DF measurements. Thirdly, we utilized attribution directed graphs to visualize DF, understand changes in DF with drowsiness, and analyze accident risks. Finally, we used DF matrices to build the IDDM. The IDDM utilized an improved adaptive genetic algorithm to extract the optimal drowsiness characteristics of individual drivers. These DF matrices, constituted by the optimal drowsiness characteristics of individual drivers, were used to train the IDDM based on principal component analysis and radial basis function neural networks. The TSTW strengthened the variation of DF with drowsiness, and the trained IDDM excavated the relationships between DF characteristics and drowsiness, which improved the accuracy and end-to-end timeliness of practical applications. The DF visualization displayed DF variations with drowsiness, theoretically supporting the use of DF to enhance personalized drowsiness driving detection.</div></div><div><h3>Results</h3><div>The DF visualization indicated drowsiness caused the distribution and transition probabilities of DF measurements to shift toward unsafe directions, thereby increasing the accident risk and demonstrating the rationality for utilizing DF to recognize drowsiness. The proposed IDDM achieved average accuracy, sensitivity, and specificity of 95.58 %, 96.50 %, and 94.70 %, respectively, outperforming most existing models. The trained IDDM demonstrated an average execution time of 0.0078 s and lower computational costs due to the reduction of PCA and simple RBFNN compared with models based on deep learning, and no requiring physiological data, which reduced invasiveness and enhanc","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107812"},"PeriodicalIF":5.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.aap.2024.107813
Zihao Li , Yang Zhou , Jiwan Jiang , Yunlong Zhang , Mihir Mandar Kulkarni
Mixed traffic environments combining human-driven vehicles (HDVs) and those equipped with Adaptive Cruise Control (ACC) have already become prevalent. This study tackles the critical yet underexplored threat of sensing attacks, such as jamming and spoofing, on ACC systems. By applying stochastically calibrated ACC and HDV car-following models grounded in field data, we constructed an integrated and high-fidelity framework to simulate mixed traffic. This allows us to comprehensively analyze traffic safety risks enabled by surrogate safety measures, under various sensing attack scenarios and considering mechanisms for cyberattack detection and human intervention. Our findings highlight profound vulnerabilities in traffic safety from sensing attacks, with factors including stochastic driving behaviors, ACC penetration rates, and attack effectiveness. Through scenario-based sensitivity analyses, this research underscores the potential risks more realistically by stochastic simulation and also contributes to the design of detection systems to safeguard mixed traffic. Ultimately, this work provides valuable insights into evaluating the robustness of ACC systems against sensing attacks, supporting the ongoing and future development of effective countermeasures.
{"title":"Adaptive Cruise Control under threat: A stochastic active safety analysis of sensing attacks in mixed traffic","authors":"Zihao Li , Yang Zhou , Jiwan Jiang , Yunlong Zhang , Mihir Mandar Kulkarni","doi":"10.1016/j.aap.2024.107813","DOIUrl":"10.1016/j.aap.2024.107813","url":null,"abstract":"<div><div>Mixed traffic environments combining human-driven vehicles (HDVs) and those equipped with Adaptive Cruise Control (ACC) have already become prevalent. This study tackles the critical yet underexplored threat of sensing attacks, such as jamming and spoofing, on ACC systems. By applying stochastically calibrated ACC and HDV car-following models grounded in field data, we constructed an integrated and high-fidelity framework to simulate mixed traffic. This allows us to comprehensively analyze traffic safety risks enabled by surrogate safety measures, under various sensing attack scenarios and considering mechanisms for cyberattack detection and human intervention. Our findings highlight profound vulnerabilities in traffic safety from sensing attacks, with factors including stochastic driving behaviors, ACC penetration rates, and attack effectiveness. Through scenario-based sensitivity analyses, this research underscores the potential risks more realistically by stochastic simulation and also contributes to the design of detection systems to safeguard mixed traffic. Ultimately, this work provides valuable insights into evaluating the robustness of ACC systems against sensing attacks, supporting the ongoing and future development of effective countermeasures.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"209 ","pages":"Article 107813"},"PeriodicalIF":5.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.aap.2024.107810
Nengchao Lyu , Zijun Du , Wei Hao
<div><div>A connected environment is crucial for improving road traffic safety and efficiency. However, it remains unclear how different connected environments affect the interaction between vehicles and their impact on driving safety and traffic efficiency in scenarios with potential risks, such as forced lane changes during emergency events. To investigate the effects of different connected environments on drivers’ interaction characteristics and their impact on driving safety and traffic efficiency, a group of simulated driving test was implemented in a multi-agent interactive intelligent connected vehicle driving simulation platform. Four types of connected environments were designed, Non-Connected Vehicles (NCV), Front Vehicle Single-Connected Vehicles (FCV), Rear Vehicle Single-Connected Vehicles (RCV), and Double-Connected Vehicles (DCV). Additionally, four different initial headways were tested (10 m, 20 m, 30 m, and 40 m). 40 drivers were recruited to participate in driving simulation experiments, and simulated driving data were collected. The research results indicate that for the front vehicle (FV), connectivity significantly reduces the collision risk with the accident vehicle (TTC<sub>FCV</sub> = 4.238 s, TTC<sub>DCV</sub> = 4.385 s), decreases the maximum longitudinal deceleration of FV (FCV = −1.212 m/s<sup>2</sup>, DCV = −1.022 m/s<sup>2</sup>), and reduces the speed fluctuation of FV (FCV = 4.748 km/h, DCV = 3.784 km/h). For the rear vehicle (RV), benefits are observed only in the FCV environment, where connectivity helps reduce the maximum deceleration of RV (FCV = −1.545 m/s<sup>2</sup>), decrease its speed fluctuation (FCV = 3.852 km/h), and enhance overall traffic efficiency (FCV = 12.133 s). Additionally, the minimum time difference to collision (TDTC) in the RCV environment (2.679 s) is significantly higher compared to other connected environments, and the number of cases with TDTC < 1.5 s (49) is notably lower than in other connected environments (NCV = 101, FCV = 107, DCV = 80). This suggests that the RCV environment effectively reduces the lateral collision risk during lane changes. Overall, while single-vehicle connectivity may help reduce driving risks and improve traffic efficiency, DCV may not significantly enhance vehicle safety and traffic efficiency. When the vehicle headway between FV and RV is 20 m (1.651 s), lateral conflicts between the vehicles are most severe. The maximum longitudinal deceleration of FV and RV also significantly decreases with increasing vehicle headway, and when the vehicle headway exceeds 30 m, the maximum longitudinal deceleration of RV nearly ceases to decrease (−1.993 m/s<sup>2</sup> at 30 m, −1.948 m/s<sup>2</sup> at 40 m). As the distance between the front and rear vehicles (DHW<sub>FV-RV</sub>) increases, the speed of FV becomes more stable, particularly when DHW<sub>FV-RV</sub> is 40 m (M = 4.204 km/h), where the speed fluctuations of FV are significantly lower compared to other
{"title":"Does connected environment contribute to the driving safety and traffic efficiency improvement in emergency events?","authors":"Nengchao Lyu , Zijun Du , Wei Hao","doi":"10.1016/j.aap.2024.107810","DOIUrl":"10.1016/j.aap.2024.107810","url":null,"abstract":"<div><div>A connected environment is crucial for improving road traffic safety and efficiency. However, it remains unclear how different connected environments affect the interaction between vehicles and their impact on driving safety and traffic efficiency in scenarios with potential risks, such as forced lane changes during emergency events. To investigate the effects of different connected environments on drivers’ interaction characteristics and their impact on driving safety and traffic efficiency, a group of simulated driving test was implemented in a multi-agent interactive intelligent connected vehicle driving simulation platform. Four types of connected environments were designed, Non-Connected Vehicles (NCV), Front Vehicle Single-Connected Vehicles (FCV), Rear Vehicle Single-Connected Vehicles (RCV), and Double-Connected Vehicles (DCV). Additionally, four different initial headways were tested (10 m, 20 m, 30 m, and 40 m). 40 drivers were recruited to participate in driving simulation experiments, and simulated driving data were collected. The research results indicate that for the front vehicle (FV), connectivity significantly reduces the collision risk with the accident vehicle (TTC<sub>FCV</sub> = 4.238 s, TTC<sub>DCV</sub> = 4.385 s), decreases the maximum longitudinal deceleration of FV (FCV = −1.212 m/s<sup>2</sup>, DCV = −1.022 m/s<sup>2</sup>), and reduces the speed fluctuation of FV (FCV = 4.748 km/h, DCV = 3.784 km/h). For the rear vehicle (RV), benefits are observed only in the FCV environment, where connectivity helps reduce the maximum deceleration of RV (FCV = −1.545 m/s<sup>2</sup>), decrease its speed fluctuation (FCV = 3.852 km/h), and enhance overall traffic efficiency (FCV = 12.133 s). Additionally, the minimum time difference to collision (TDTC) in the RCV environment (2.679 s) is significantly higher compared to other connected environments, and the number of cases with TDTC < 1.5 s (49) is notably lower than in other connected environments (NCV = 101, FCV = 107, DCV = 80). This suggests that the RCV environment effectively reduces the lateral collision risk during lane changes. Overall, while single-vehicle connectivity may help reduce driving risks and improve traffic efficiency, DCV may not significantly enhance vehicle safety and traffic efficiency. When the vehicle headway between FV and RV is 20 m (1.651 s), lateral conflicts between the vehicles are most severe. The maximum longitudinal deceleration of FV and RV also significantly decreases with increasing vehicle headway, and when the vehicle headway exceeds 30 m, the maximum longitudinal deceleration of RV nearly ceases to decrease (−1.993 m/s<sup>2</sup> at 30 m, −1.948 m/s<sup>2</sup> at 40 m). As the distance between the front and rear vehicles (DHW<sub>FV-RV</sub>) increases, the speed of FV becomes more stable, particularly when DHW<sub>FV-RV</sub> is 40 m (M = 4.204 km/h), where the speed fluctuations of FV are significantly lower compared to other ","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107810"},"PeriodicalIF":5.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1016/j.aap.2024.107803
Shixuan Weng , Chen Chai , Weiru Yin , Yanbo Wang
Takeover performance is a crucial constraint on deploying Level 3 automated driving. Not all drivers can adopt appropriate strategies to take over vehicle control during safety–critical situations. The hazard perception abilities of novice drivers may cause individual differences in urgent takeover performance. This research examines the urgent takeover performances of novice drivers with different hazard perception abilities for takeover safety improvement. Forty novice drivers took over in urgent cut-in situations at a driving simulator. The hazard perception tests evaluated their hazard perception abilities. This study formulated moderating effect models based on experimental data. Results indicated that hazard perception ability indirectly affected the significance of the correlation between takeover reaction and steering behaviors. Drivers with improved hazard perception abilities are less likely to turn sharply on the steering wheel. In this study, 39.8% of the participants need to improve their hazard perception abilities. Their z-scores were longer than 0.002 in hazard perception tests. Findings can identify the individuals who need hazard perception training to enhance their takeover performance effectively.
接管性能是部署三级自动驾驶的一个关键制约因素。并非所有驾驶员都能采取适当的策略，在安全关键时刻接管车辆控制权。新手驾驶员的危险感知能力可能会导致紧急接管性能的个体差异。本研究考察了具有不同危险感知能力的新手驾驶员的紧急接管表现，以提高接管的安全性。40 名新手驾驶员在驾驶模拟器上进行了紧急切入情况下的接管。危险感知测试评估了他们的危险感知能力。本研究根据实验数据建立了调节效应模型。结果表明，危险感知能力间接影响了接管反应和转向行为之间的相关性。危险感知能力提高的驾驶员更不可能急转方向盘。在本研究中，39.8% 的参与者需要提高危险感知能力。在危险感知测试中，他们的 Z 值大于 0.002。研究结果可以确定哪些人需要接受危险感知培训，以有效提高他们的接管性能。
{"title":"Identifying novice drivers in need of hazard perception ability improvement for takeover performance in Level 3 automated driving","authors":"Shixuan Weng , Chen Chai , Weiru Yin , Yanbo Wang","doi":"10.1016/j.aap.2024.107803","DOIUrl":"10.1016/j.aap.2024.107803","url":null,"abstract":"<div><div>Takeover performance is a crucial constraint on deploying Level 3 automated driving. Not all drivers can adopt appropriate strategies to take over vehicle control during safety–critical situations. The hazard perception abilities of novice drivers may cause individual differences in urgent takeover performance. This research examines the urgent takeover performances of novice drivers with different hazard perception abilities for takeover safety improvement. Forty novice drivers took over in urgent cut-in situations at a driving simulator. The hazard perception tests evaluated their hazard perception abilities. This study formulated moderating effect models based on experimental data. Results indicated that hazard perception ability indirectly affected the significance of the correlation between takeover reaction and steering behaviors. Drivers with improved hazard perception abilities are less likely to turn sharply on the steering wheel. In this study, 39.8% of the participants need to improve their hazard perception abilities. Their z-scores were longer than 0.002 in hazard perception tests. Findings can identify the individuals who need hazard perception training to enhance their takeover performance effectively.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107803"},"PeriodicalIF":5.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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