Safety Performance Assessment of Connected Vehicles in Mitigating the Risk of Secondary Crashes: A Driving Simulator Study

Gaweesh, Sherif; Bakhshi, Arash Khoda; Ahmed, Mohamed M.

doi:10.1177/03611981211027881

Cited by 24 publications

(11 citation statements)

References 50 publications

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“…The risk of expressway crashes was decreased and the efficiency was increased. Gaweesh et al [19] used a driving simulator to design a CV scenario and a non-CV scenario in bad weather. The vehicle kinematic data obtained from the experiments were analyzed.…”

Section: Literature Review 21 Safety Management In Expressway Accident Segmentsmentioning

confidence: 99%

Drivers’ Decelerating Behaviors in Expressway Accident Segments under Different Speed Limit Schemes

Zhang

et al. 2022

IJERPH

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Traffic accidents occurring on expressways tend to give rise to traffic bottlenecks. To ensure the vehicles safely and smoothly pass through the accident segments, speed limits are generally taken to regulate the vehicles’ movements. This study aims to explore the decelerating behaviors of drivers under different speed limit schemes. We designed traffic accident scenarios under four speed limit schemes using the driving simulator. A total of 60 subjects drove the simulator passing the accident segments according to their habits. The vehicles’ kinematic data and the subjects’ operating data were recorded. To further analyze the drivers’ decelerating behaviors in different speed limit scenarios, driving experience was also taken into account. The results show that the speed limit schemes have significant effects on drivers’ decelerating behaviors. The more speed limit signs there are, the smoother the decelerating process will be. Driving experience significantly affects some of the decelerating parameters, including the location of deceleration starting point, average deceleration, and locations of decelerating to the initial and final speed limits. These results provide a theoretical basis for traffic safety and driving behavior management.

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Section: Literature Review 21 Safety Management In Expressway Accident Segmentsmentioning

confidence: 99%

Drivers’ Decelerating Behaviors in Expressway Accident Segments under Different Speed Limit Schemes

Zhang

et al. 2022

IJERPH

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“…However, there is a lack in safety performance assessment of CV technology in the combined effect of these two factors. The current study aims to bridge this gap, based on a high-fidelity DS experiment which has been widely followed in the literature, to assess the effectiveness of CV applications, utilizing WyoSafeSim (18,32,(40)(41)(42)(43)(44).…”

Section: Introductionmentioning

confidence: 99%

Driving Simulator Trajectory-Level Analysis of Truck Drivers’ Behavioral Alteration in Connected Vehicles Environment Under Fog with Complex Roadway Geometry

Bakhshi

Ahmed

2022

Transportation Research Record: Journal of the Transportation R

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Foggy weather increases crash likelihood when coinciding with roadway geometry changes inconsistent with drivers’ expectations. The situation might be exacerbated for heavy trucks having to evade critical safety events because of the vehicles’ maneuverability limitations, imposing prime safety challenges on major freight corridors like Interstate-80 (I-80) in the U.S. Aligned with the connected vehicle (CV) pilot program on I-80 in Wyoming, this study intends to unveil how CV technology alleviates safety concerns in this regard. To this aim, a with/without analysis approach was performed utilizing a high-fidelity truck driving simulator. Twenty-three professional truck drivers were recruited to drive the simulator in CV scenario with traveler information messages, including foggy weather ahead and an advisory speed of 45 mph, and in a non-CV counterpart without notifications. Longitudinal and lateral drivers’ behaviors were quantified by kinematic-based surrogate measures of safety (K-SMoS) characterized on vehicles’ trajectory, including longitudinal speed, lateral speed, steering, their corresponding spatial standard deviations, and the coefficient of variation of longitudinal speed. The central tendency and dispersion of K-SMoS distributions were compared between CVs and non-CVs throughout the simulated roadway. Results showed immediate truck drivers’ compliance to CV notifications, which was more apparent in their longitudinal driving behaviors. On a horizontal curve with poor visibility, statistically significant reductions in central tendency and dispersion of K-SMoS distributions up to 67% in CVs were observed, minimizing the crash risk in CV environments. Besides, findings revealed that exposure to the CV notifications minimized drivers’ behavior uncertainty, manifesting in their improved situational awareness and enhancing the safety performance of the traffic stream.

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“…Studies for enhancing safe driving encounter the issue that experimental validity should not be achieved at the expense of the safety of the humans involved in the experiment [7], so they must be simulation-based. For this reason, the research community has increasingly focused on virtual reality (VR) simulators: for example, Bozkir et al [8] aimed to use VR to train drivers in critical situations, Caruso et al [9] assessed the impact of the level of detail (LOD) on the drivers' behavior, Gaweesh et al [10] evaluated safety performance of connected vehicles in mitigating the risk of secondary crashes, and Bakhshi et al [11] focused on scenarios which involved truck drivers. However, researchers must be Appl.…”

Section: Introductionmentioning

confidence: 99%

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

et al. 2021

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Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

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Safety Performance Assessment of Connected Vehicles in Mitigating the Risk of Secondary Crashes: A Driving Simulator Study

Cited by 24 publications

References 50 publications

Drivers’ Decelerating Behaviors in Expressway Accident Segments under Different Speed Limit Schemes

Drivers’ Decelerating Behaviors in Expressway Accident Segments under Different Speed Limit Schemes

Driving Simulator Trajectory-Level Analysis of Truck Drivers’ Behavioral Alteration in Connected Vehicles Environment Under Fog with Complex Roadway Geometry

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

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