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

2019 ◽  
Vol 2 (2) ◽  
pp. 78-90 ◽  
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.

scholarly journals An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

2020 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Haotian Cao ◽  
Zhenghao Zhang ◽  
Xiaolin Song ◽  
Hong Wang ◽  
Mingjun Li ◽  
...  
Keyword(s):  
Cell Phone ◽  
Large Scale ◽  
Population Attributable Risk ◽  
Demographic Characteristics ◽  
Driving Safety ◽  
Crash Risk ◽  
Lower Percentage ◽  
Content Type ◽  
Driving Experience ◽  
Naturalistic Driving

Purpose The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages. Design/methodology/approach A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions. Findings Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving. Originality/value The results provide support to guide countermeasures and vehicle design.

scholarly journals Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6537 ◽  
Author(s):  
Kevin Bylykbashi ◽  
Ermioni Qafzezi ◽  
Phudit Ampririt ◽  
Makoto Ikeda ◽  
Keita Matsuo ◽  
...  
Keyword(s):  
Real Time ◽  
Support System ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Autonomous Vehicle ◽  
Health Condition ◽  
Driving Safety ◽  
Risk Level ◽  
Vehicle Speed ◽  
Driver Assistance Systems

The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver’s ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xuewei Li ◽  
Yuchen Jia ◽  
Yufei Chen ◽  
Guanyang Xing ◽  
Xiaohua Zhao ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Safety Evaluation ◽  
Warning System ◽  
Sample Entropy ◽  
Driving Behavior ◽  
Driving Safety ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
General Evaluation ◽  
Speed Stability

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.

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

Information ◽  
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.

scholarly journals Abnormal sleep duration and motor vehicle crash risk

2004 ◽  
Vol 13 (2) ◽  
pp. 177-178 ◽  
Author(s):  
Nathaniel S. Marshall ◽  
Warren Bolger ◽  
Philippa H. Gander
Keyword(s):  
Sleep Duration ◽  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Crash Risk ◽  
Vehicle Crash ◽  
Abnormal Sleep

Knowledge assimilation by employees in learning organizations

2014 ◽  
Vol 21 (6) ◽  
pp. 350-368 ◽  
Author(s):  
Tali Marcus ◽  
Snunith Shoham
Keyword(s):  
Self Efficacy ◽  
Safety Information ◽  
Future Research ◽  
Subjective Perception ◽  
Content Type ◽  
Unique Nature ◽  
Knowledge Assimilation ◽  
The Subject ◽  
Powerful Explanation ◽  
Safety Procedures

Purpose – The purpose of this study is to examine the factors related to the employee as an individual, that affect the quality and level of the individual’s assimilation of knowledge (AOK) which is transmitted by way of organizational learning. Design/methodology/approach – All subjects (317) of this research were employed at different positions in day camps of a social organization. The study examined the subjects’ AOK relating to the organization’s security and safety procedures. The variables examined in this study include: the employee’s organizational commitment; the employee’s perception of the organization’s culture; the employee’s perception of the advantage inherent in the security and safety information; the employee’s self-efficacy; and the employee’s motivation to assimilate the new knowledge. Findings – The research variables explained a significant part (37 per cent) of the variance obtained with respect to assimilation and learning in the organization. The most powerful explanation for the variance in degree of implementation was the perception of the organization’s security and safety culture and the subject’s self-efficacy. Subjects’ perceived advantage from the knowledge did not make a significant contribution and motivation serves as a mediator but it does not mediate directly between the variables and AOK. Research limitations/implications – The research was conducted in a single organization. We recommend conducting similar studies in other organizations, including other types of organizations, to strengthen the conclusions which derive from our research. We also recommend that future research should use alternative methodologies (e.g. qualitative research and review of the results by experts) since other methodologies might reveal new facts that may have been uncovered in the use of the quantitative method applied in our research. Practical implications – We recommend that an organization which strives to be a learning organization, should pay attention, inter alia, to factors relating to the employees themselves, and in particular: increasing the employees’ self-efficacy, clarifying the benefits to the employee of the transmitted knowledge; and bringing the organization’s values and culture into clearer focus for the employees. Originality/value – The unique nature of our research model is twofold: first, the variables on which we have chosen to focus are different from other studies, and to our knowledge, the combination of these variables and the examination of these variables in relation to learning in the context of organizations have not been examined in other studies. Second, our model gauges the effects of an employee’s subjective perception with relation to his organization’s culture, his perceived advantage with regard to the subject-matter which he is learning and his self-assessed existing knowledge.

What is the motor vehicle crash risk for drivers with epilepsy? A systematic review

2021 ◽  
Vol 23 ◽  
pp. 101286
Author(s):  
Sjaan Koppel ◽  
Marilyn Di Stefano ◽  
Bleydy Dimech-Betancourt ◽  
Mohammed Aburumman ◽  
Rachel Osborne ◽  
...  
Keyword(s):  
Systematic Review ◽  
Motor Vehicle ◽  
Motor Vehicle Crash ◽  
Crash Risk ◽  
Vehicle Crash

Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

2021 ◽  
pp. 036119812110444
Author(s):  
Megat-Usamah Megat-Johari ◽  
Nusayba Megat-Johari ◽  
Peter T. Savolainen ◽  
Timothy J. Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Negative Binomial ◽  
Driver Behavior ◽  
Field Research ◽  
Crash Risk ◽  
Traffic Crashes ◽  
Crash Data ◽  
Safety Issues ◽  
Dynamic Message Signs ◽  
Safety Message ◽  
Message Signs

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

Value in vehicles: economic assessment of automotive data

2018 ◽  
Vol 20 (6) ◽  
pp. 513-527
Author(s):  
Alexander M. Soley ◽  
Joshua E. Siegel ◽  
Dajiang Suo ◽  
Sanjay E. Sarma
Keyword(s):  
Model Performance ◽  
Economic Assessment ◽  
Connected Vehicle ◽  
Data Generation ◽  
Content Type ◽  
Monetary Value ◽  
Vehicle Data ◽  
Data Market ◽  
Data Stewardship ◽  
Value Model

Purpose The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers. Design/methodology/approach The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors. Findings Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn. Research limitations/implications This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions. Practical implications The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship. Social implications Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens. Originality/value This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

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