Safety Evaluation of Fog Warning Systems in a Connected Vehicle Environment Based on Sample Entropy

Changes in driving behavior caused by reduced visibility in fog can lead to crashes. To improve driving safety in fog weather, a fog warning system based on connected vehicle (CV) technology is proposed. From the perspective of human factors, this study evaluates the driving safety based on drivers’ speed change under different fog levels (i.e., no fog, light fog, and heavy fog) and different technical levels (i.e., normal, with a dynamic message sign (DMS), and with a human-machine interface (HMI)). The driving behavior data were collected by a driving simulation experiment. The experimental road was divided into three zones: clear zone, transition zone, and fog zone. To quantify the change of vehicle speed comprehensively, the speed and acceleration were selected. Meanwhile, the vehicle speed safety entropy and acceleration safety entropy were proposed based on sample entropy theory. Furthermore, the changes of each index in different zones were analyzed. The results show that the use of fog warning system can improve speed stability and driving safety in fog zones and can make the driver decelerate in advance with a smaller deceleration before entering the fog zones. The higher the technical level is, the earlier the driver decelerates. Under the condition of light fog, the fog warning system with HMI has a better effect in terms of improving speed stability, while under the condition of heavy fog, there is little difference between the two technical levels. In general, this study proposed a novel safety evaluation index and a general evaluation method of the fog warning system.

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Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study

Sensors ◽

10.3390/s20092683 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2683

Author(s):

Rui Fu ◽

Yali Zhang ◽

Chang Wang ◽

Wei Yuan ◽

Yingshi Guo ◽

...

Keyword(s):

Warning System ◽

Relative Distance ◽

Driving Safety ◽

Lane Change ◽

Vehicle Speed ◽

Accuracy Rate ◽

Safety Warning ◽

Change Characteristics ◽

Speed Level

Speed has an important impact on driving safety, however, this factor is not included in existing safety warning algorithms. This study uses lane change systems to study the influence of vehicle speed on safety warning algorithms, aiming to determine lane change warning rules for different speeds (DS-LCW). Thirty-five drivers are recruited to carry out an extreme trial and naturalistic driving experiment. The vehicle speed, relative speed, relative distance, and minimum safety deceleration (MSD) related to lane change characteristics are then analyzed and calculated as warning rule characterization parameters. Lane change warning rules for a rear vehicle in the target lane under four-speed levels of 60 ≤ v < 70 km/h, 70 ≤ v < 80 km/h, 80 ≤ v < 90 km/h, and v ≥ 90 km/h are established. The accuracy of lane change warning rules not considering speed level (NDS-LCW) and ISO 17387 are found to be 87.5% and 79.8%, respectively. Comparatively, the accuracy rate of DS-LCW under four-speed levels is 94.6%, 93.8%, 90.0%, and 92.6%, respectively, which is significantly superior. The algorithm proposed in this paper provides warning in the lane change process with a smaller relative distance, and the accuracy rate of DS-LCW is significantly superior to NDS-LCW and ISO 17387.

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Prediction of instantaneous driving safety in emergency scenarios based on connected vehicle basic safety messages

Journal of Intelligent and Connected Vehicles ◽

10.1108/jicv-07-2019-0008 ◽

2019 ◽

Vol 2 (2) ◽

pp. 78-90 ◽

Cited By ~ 2

Author(s):

Kai Yu ◽

Liqun Peng ◽

Xue Ding ◽

Fan Zhang ◽

Minrui Chen

Keyword(s):

Safety Information ◽

Classification Model ◽

Driving Safety ◽

Crash Risk ◽

Risk Level ◽

Vehicle Speed ◽

Connected Vehicle ◽

Content Type ◽

Safety Message ◽

Vehicle Crash

Purpose Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency. Design/methodology/approach To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term. Findings The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages. Originality/value The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

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Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

Frontiers in Psychology ◽

10.3389/fpsyg.2021.693005 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yanqun Yang ◽

Yang Feng ◽

Said M. Easa ◽

Xiujing Yang ◽

Jiang Liu ◽

...

Keyword(s):

Physiological State ◽

Driving Behavior ◽

Driving Safety ◽

Steering Wheel ◽

Vehicle Speed ◽

Accelerator Pedal ◽

Acoustic Environment ◽

Highway Tunnel ◽

Significant Difference ◽

Sound Environment

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η2 = 0.167) and steering wheel angle (p = 0.007, η2 = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels.

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Study on the safety speed of vehicle turning with considering the driving safety evaluation

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211064222 ◽

2021 ◽

pp. 095440702110642

Author(s):

Qu Xian ◽

Yu Feng ◽

Zhang Jinlong ◽

Xie Jun

Keyword(s):

Critical Speed ◽

Comprehensive Evaluation ◽

Safety Margin ◽

Safety Evaluation ◽

Driving Safety ◽

Vehicle Speed ◽

Entropy Weight ◽

Analytic Hierarchy ◽

State Evaluation ◽

Weight Analysis

Safety speeds estimation, as an indispensable link in the development of the aided or automatic drive, receives wide attention recently. Due to uncertain disturbances of vehicle driving, it is a challenging task to estimate the safety speed credibly. This work proposes a modified estimation approach to predict the turning safety speed by combining the static drive safety evaluation with the dynamic vehicle speed calculation. First, the driving safety state is evaluated considering the coupling of driver-vehicle-road-environment, where a comprehensive evaluation is obtained by combing the analytic hierarchy process and entropy weight analysis method. Then, a turning critical speed is calculated based on the vehicle driving dynamics considering both sideslip and rollover. The estimation of turning safety speed is achieved by modifying the critical speed with a safety correction factor obtained from the driving safety state evaluation. Finally, cases discussion on driving safety states evaluation, as well as the critical speed verification and safety speed analysis, are carried out. The results verify the validity of the driving safety state evaluation and critical speed calculation. The safety speeds have a reasonable safety margin according to the driving safety state evaluation. The maximum differences between the safety speeds and critical speeds are about 26.45% for buses and 26.39% for cars under low adhesion conditions, showing sufficient reliability for safety speed estimation.

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Developing a Crash Warning System for the Bike Lane Area at Intersections with Connected Vehicle Technology

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119840617 ◽

2019 ◽

Vol 2673 (4) ◽

pp. 47-58 ◽

Cited By ~ 7

Author(s):

Yina Wu ◽

Mohamed Abdel-Aty ◽

Ou Zheng ◽

Qing Cai ◽

Lishengsa Yue

Keyword(s):

Traffic Safety ◽

Warning System ◽

Threshold Value ◽

Vehicle Speed ◽

Connected Vehicle ◽

Trajectory Data ◽

Vehicle Trajectories ◽

Connected Vehicle Technology ◽

Turning Maneuver ◽

The Right

A common type of bike lane at intersections is between the through lane and the right lane. With such design, right-turning drivers need to cross the bike lane to merge into the right lane, which could cause conflicts with bicycles on the keyhole bike lane. This study aims to develop a warning system for drivers to avoid vehicle–bicycle crashes in the bike lane area under a connected vehicle environment. To propose a reasonable warning system, 118 right-turning vehicle trajectories were collected by an unmanned aerial vehicle. Drivers’ right-turning behaviors are investigated based on the trajectory data. Then, a vehicle–bicycle crash warning algorithm is proposed to calculate the post-encroachment time (PET) under different situations. By comparing the threshold value and the PET value, potential vehicle–bicycle crash locations in the bike lane area could be identified. The proposed algorithm is designed to be displayed on front windshields with an augmented reality display. The results suggested that the proposed algorithm could provide high prediction accuracy. Moreover, vehicle speed, vehicle location, bicycle speed, and bicycle location were found to have significant impact on the locations of dangerous areas. It is expected that the proposed warning system could be used to identify the dangerous areas and deliver warning information for right-turning drivers when they are approaching an intersection. The warning system could help drivers be more prepared for the upcoming right-turning maneuver, and thus improve traffic safety for both drivers and cyclists at intersections.

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Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Energies ◽

10.3390/en14248429 ◽

2021 ◽

Vol 14 (24) ◽

pp. 8429

Author(s):

Liang Chen ◽

Jiming Xie ◽

Simin Wu ◽

Fengxiang Guo ◽

Zheng Chen ◽

...

Keyword(s):

Driving Simulator ◽

Short Term Memory ◽

Road Traffic ◽

Lateral Displacement ◽

Safety Evaluation ◽

Driving Behavior ◽

Steering Wheel ◽

Vehicle Speed ◽

Driving Simulators ◽

Convenient Setting

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

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Effect of Warning System on Interactive Driving Behavior at Unsignalized Intersection under Fog Conditions: A Study Based on Multiuser Driving Simulation

Journal of Advanced Transportation ◽

10.1155/2020/8871875 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Yunfan Zhang ◽

Xuedong Yan ◽

Jiawei Wu ◽

Ke Duan

Keyword(s):

Collision Avoidance ◽

Warning System ◽

Driving Behavior ◽

Driving Simulation ◽

Driving Safety ◽

Collision Warning ◽

Action Point ◽

Increased Risk ◽

Simulation Scenario ◽

Further Development

The intersection collision warning system (ICWS) is an emerging technology designed to assist drivers in avoiding collisions at intersections. ICWS has an excellent performance in reducing the number of collisions and improving driving safety. Previous studies demonstrated that when visibility was low under fog conditions, ICWS could help drivers timely detect hazardous conflicting vehicles. However, the influences of ICWS on interactive driving behavior at unsignalized intersection between different vehicles have barely been discussed. This study aimed to investigate the patterns of drivers’ interactive behaviors with the assistance of a new kind of ICWS under fog conditions based on Multiuser Driving Simulation. The Multiuser Driving Simulation allowed multiple drivers to operate in the same simulation scenario at the same time, and it could capture drivers’ interactions preferably. Forty-eight licensed drivers completed the driving simulation experiment in three fog conditions (no fog, light fog, and heavy fog) and two warning conditions (warning and no warning), in which the drivers drove in a straight-moving situation at unsignalized intersection with potential collision risks caused by the encounter of two vehicles. The results verified that warning and fog conditions were significant factors that affected the interactive driving behavior in the unsignalized intersection collision avoidance process, including the driver’s decision, TTC of action point, average acceleration (deceleration) rate, and postencroachment time. Compared to conditions without ICWS, the ICWS could help drivers make collision avoidance actions earlier and change the speed more smoothly. In addition, with the help of Multiuser Driving Simulation, associations between decision driving behaviors of vehicles were discussed with caution. The results revealed the decision-making mechanism of drivers in the process of interaction with other drivers. Under the influence of fog, interactive driving processes were fraught with increased risk at unsignalized intersection. However, the ICWS helped drivers interact more harmoniously, safely, and efficiently. The findings shed some light on the further development of ICWS and the study on interactive driving behavior.

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Analysis of EEG Characteristics of Drivers and Driving Safety in Undersea Tunnel

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18189810 ◽

2021 ◽

Vol 18 (18) ◽

pp. 9810

Author(s):

Yongzheng Yang ◽

Zhigang Du ◽

Fangtong Jiao ◽

Fuquan Pan

Keyword(s):

Evaluation Model ◽

Jiaozhou Bay ◽

Safety Evaluation ◽

Driving Safety ◽

Vehicle Speed ◽

Distribution Law ◽

Change Rate ◽

Power Change ◽

Highly Correlated ◽

Real Vehicle

To study the influence of the driving environment of an undersea tunnel on driver EEG (electroencephalography) characteristics and driving safety, a real vehicle experiment was performed in the Qingdao Jiaozhou Bay Tunnel. The experimental data of the drivers’ real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The undersea tunnel is divided into different areas, and the distribution law of driving environment characteristics, EEG characteristics and vehicle speed characteristics is analyzed. The correlations between the driving environment characteristics, EEG characteristics and vehicle speed characteristics model the variables that pass the correlation test. The driving safety evaluation model of an undersea tunnel is established, and the driving safety in different areas of the undersea tunnel is evaluated. The results show that there are obvious differences in illumination, EEG power change rate, vehicle speed and other variables in different areas of the undersea tunnel. The driving environment characteristics are highly correlated with the β wave power change rate. The driving safety of different areas of the undersea tunnel from high to low is: upslope area, downslope area, exit area and entrance area. The study will provide a theoretical basis for the safe operation of the undersea tunnel.

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Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

Information ◽

10.3390/info12050194 ◽

2021 ◽

Vol 12 (5) ◽

pp. 194

Author(s):

Hussein Ali Ameen ◽

Abd Kadir Mahamad ◽

Sharifah Saon ◽

Rami Qays Malik ◽

Zahraa Hashim Kareem ◽

...

Keyword(s):

Liquid Crystal Display ◽

Driving Behavior ◽

Low Noise ◽

Light Signal ◽

Classification Model ◽

Driver Model ◽

Driving Safety ◽

Vehicle Speed ◽

Driver Assistance Systems ◽

Safe Driving

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

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Research on Oil Depot Safety Evaluation Based on Dow Chemical Fire and Explosion Index Assessment Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.748.1256 ◽

2013 ◽

Vol 748 ◽

pp. 1256-1261

Author(s):

Shou Hui He ◽

Han Hua Zhu ◽

Shi Dong Fan ◽

Quan Wen

Keyword(s):

Production Management ◽

Evaluation Method ◽

Risk Index ◽

Safety Evaluation ◽

Assessment Method ◽

Theoretical Ground ◽

Potential Hazard ◽

Process Unit ◽

Safety Production ◽

Fire And Explosion

At the present time, the Dow Chemical Fire and Explosion Index (F&EI) is a kind of risk index evaluation method that is comprehensively used in evaluating potential hazard, area of exposure, expected losses in case of fire and explosion, etc. As the research object to oil depot storage tank area, this article ultimately confirms establishing appropriate pattern of process unit as well as reasonable safety precautions compensating method, in order to insure the reasonableness of evaluating result, by means of selecting process unit, confirming material factor and compensating safety precautions, using F&EI method. This can provide the basis for theoretical ground in aspect of oil depot development and safety production management.

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