Driver Awareness Research Articles

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

Supporting driver performance in closely spaced tunnel-interchange structure: Traffic control devices and effects on driving behavior

Objectives With the rapid development of expressways in the mountainous regions of southwestern China, closely spaced tunnel-interchange structures have inevitably emerged due to topographical constraints and environmental limitations. Given the unfavorable road geometry and rapid cross-section transitions, drivers face significant safety concerns. This study aims to investigate drivers’ safety performance at closely spaced tunnel-interchange sections and determine how safety risks can be mitigated through improved traffic control devices design. Methods Thirty-nine participants conducted an experimental study in a fixed-base simulator. The test scenario was modeled on the Xingyan Freeway-S3801 and accurately reproduced in the simulator. For each safety performance metric, the driving simulator experiments yielded a dataset with 780 observations. To address the idiosyncratic variation due to individual driver differences, a series of linear mixed effects models (LMM) were developed to analyze drivers’ behavior responses. Results In closely spaced tunnel-interchange sections, a general impairment of both longitudinal and lateral performance was observed. This study identified potential critical impact variables in traffic control device systems. According to the LMM results: (a) Removing the 0.5 km interchange ramp exit advance guide sign located in the tunnel exit area reduces dangerous behavior in the corresponding impact area. (b) Replacing the 0.5 km interchange ramp exit advance guide sign with arrow pavement markers as an information source supports improved driver performance, promoting driver safety. (c) Adding tunnel exit distance signs within tunnels is recommended to enhance situation awareness for drivers. Conclusions This study addresses the scientific issues related to traffic control devices setup for closely spaced tunnel-interchange sections, focusing on identifying potential critical impact variables. The findings provide guidance on the design of traffic control devices for such sections and support revisions to national engineering standards.

Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF

Speed Corridor Enforcement System (SCES) - A Case Study of the Highways of Kosovo

Background Traffic safety remains a significant global concern. Despite advancements in vehicle technology, road infrastructure improvements, and initiatives to raise driver awareness, traffic accidents continue to pose socio-economic problems. Objectives The aim of this study was to suggest the potential benefits of implementing a Speed Corridor Enforcement System on highways in Kosovo, aiming to reduce accidents and promote driver awareness. Methods To accomplish the stated objective, there is employed a scientific research methodology supported by surveys, statistics, and analysis methods. The sources of information comprise both primary and secondary data, which were gathered from scientific literature, online sources, and relevant studies in the field. Results The significant socio-economic challenges arising from traffic accidents are discussed, highlighting the necessity for a comprehensive and multidimensional approach to effectively address this issue. Additionally, the proposal of implementing Speed Control Enforcement Systems on the highways of Kosovo, which are part of Intelligent Transport Systems, is emphasized as a solution. Emphasis is placed on the importance of adopting advanced technological solutions alongside comprehensive strategies to effectively tackle the complex problem of traffic safety. Conclusion Considering the alarming 53.41% increase in accidents in Kosovo, the implementation of the speed identification system in corridors could have initially prevented and reduced accidents by a percentage similar to those observed in the reference countries.

Metacognitive Awareness of Older Adult Drivers with Mild Cognitive Impairment: Relationships with Demographics, Subjective Evaluation of Cognition, and Driving Self-Efficacy.

(1) Self-regulation of driving is a means of maintaining one's driving identity. The purpose of this study was to investigate the extent to which older drivers with Mild Cognitive Impairment (MCI) are metacognitively aware of the requirements of specific demanding driving conditions and whether this awareness is linked to subjective assessments of cognition. (2) One hundred seventeen (117) older MCI drivers participated in a telephone survey in which they reported their metacognitive experiences in nine driving conditions, listed as an aim of self-regulation. The analyses included the participants' subjective cognitive assessments, both in terms of their cognitive state and their perceived driving self-efficacy. (3) The analyses pointed out a direct and negative effect of age on the formation of the metacognitive feeling of certainty. Furthermore, an indirect effect of sex through driving self-efficacy was established. This effect was negative in the case of the metacognitive feeling of difficulty and the estimation of effort and positive in the case of the metacognitive feeling of certainty. (4) This position points out the need to establish appropriate levels of the perceived self-efficacy of older drivers with MCI, and it raises issues when it moves to fictitious levels.

Road Bump Detection and Voice Alert System Using YOLOv8 and gTTS

Roadway bumps present a significant hazard to drivers, leading to discomfort, vehicle damage, and even accidents. This research paper proposes an innovative solution for bump detection and driver/vehicle response using advanced artificial intelligence (AI) techniques. This research paper explores the application of the YOLOv8 algorithm for detecting road bumps and generating voice alerts to enhance road safety. The proposed system utilizes a camera based approach installed in vehicles to detect road bumps in real-time. Upon detection, a voice alert is generated using the gTTS library, informing the driver of an impending bump ("Bump is near you"). The primary objective is to provide drivers with timely information to reduce vehicle speed and avoid discomfort or potential accidents caused by road bumps. Additionally, the paper discusses the feasibility of integrating this AI algorithm into car systems to automate speed reduction when bumps are detected. By leveraging sensors and advanced algorithms, car companies could enhance vehicle safety by ensuring that vehicles automatically slow down upon approaching a road bump, mitigating the risk of accidents and minimizing wear and tear on vehicles. Furthermore, the study investigates the impact of road bumps on accidents, emphasizing the importance of early detection and driver awareness. By addressing this issue, the proposed system aims to contribute to improved road safety, reduced accident rates, and enhanced driving experience for all road users.

Evaluation of eye-catching effect in highway tunnel entrance area based on saccade behavior

Objective This study aimed to investigate the effects of visual attraction in the entrance areas of highway tunnels on drivers’ saccade behavior and driving safety, with the objective of providing insights for tunnel entrance design and driver education. Methods Fifty participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the scanning behavior of participating drivers. Various metrics, including saccade duration, frequency, amplitude, velocity, and the peak-to-average ratio of saccade velocity (PARSV), were measured and compared across different scenarios. Results The results revealed different patterns of visual scanning dynamics among the four experimental scenarios, reflecting the different levels of visual-cognitive demands and situation awareness of drivers. The visual attraction in the tunnel entrance area resulted in prolonged saccade duration, increased saccade amplitude, reduced saccade frequency, and slower, less stable saccade velocity. Among these, drivers exhibited the most intricate and unstable scanning behavior, accompanied by the lowest level of situational awareness, particularly under the influence of visual attraction of tip slogan. Conclusions The findings highlight the importance of considering visual attraction in tunnel entrance design. Minimizing unnecessary visual attraction can help reduce distractions and maintain driver attention, ultimately enhancing driving safety. Driver education programs should emphasize the significance of adapting to visual attraction at tunnel entrances to promote safer driving practices. Overall, this study contributes valuable insights into the effects of visual attraction on driver scanning behavior, facilitating the development of strategies to improve tunnel design and driver education.

RISK BEHAVIORS AMONG DRIVERS: AN ASSESSMENT BASED ON RESEARCH

The study was planned to analyze traffic behavior undertaken by drivers, including the emerging so-called awareness gaps. The study was conducted in Poland, and 211 drivers participated in it. The results show many important relationships that could form the basis for proposing effective social campaigns on road safety, including increasing the awareness of drivers as those who have a real impact on its improvement. The analyses confirm the existence of the awareness gap in the examined target group. They also show what kinds of behaviors are considered by drivers to be risky and what kinds of behaviors are aggressive. Moreover, they show the percentage of undesirable behaviors in road traffic and indicate which behaviors are considered by drivers to be the most threatening.

Awareness and Practice on Road Safety among Adults in Selected Area of Jalandhar Cantt, Punjab

It has been estimated that one million death and 15 million road side accident occur on the road worldwide. Globally road side accidents are the 7th leading cause of death in all age groups. In India we have 1% of the world vehicle population, but 6% of the world accidents 1. The aim of the study is to assess the awareness and practice on road safety among adults in in selected area of Jalandhar Cantt, Punjab. A total of 108 samples were selected for the study and samples were the adults with in the age group of 18-60 years. The major findings of the study were as follows: The study analysis showed that majority (69%) of the samples were males and 60% belonged to the group of 18-30 years of age. 15% of samples were drivers by profession, 63.8% had a valid driving license. Majority of samples, (83.2%) had good awareness on road safety and 94% of samples had good practice on pedestrian road safety. The study showed that there was a low positive and significant correlation between awareness and practice among pedestrians (n=400) as the p value is .001 and spearman’s rho value is 0.269. And there was a low and significant correlation between awareness and practice of drivers the p value is .001 and spearman’s rho value for 0.322.

An advanced multimodal driver-assistance prototype for emergency-vehicle detection

In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver’s awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver’s facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road.

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

The conceptual framework for assessing the situational awareness (SA) of drivers consists of three hierarchical levels: perception of the elements of the environment, comprehension of the elements, and decision-making in the near future. A common challenge in evaluating SA is the determination of the available subjective and objective techniques and their selection and integration into methodologies. Among the objective techniques, eye tracking is commonly used, considering the influence of gaze behavior on driving. This review is presented as an innovative approach to the subject matter, introducing physiological metrics based on eye tracking and investigating their application in assessing the SA of drivers. In addition, experiments and methodologies that revealed patterns at the three levels of SA were identified. For this purpose, databases were searched, and 38 papers were considered. Articles were clustered according to prevalent themes such as eye-tracking metrics, eye-tracking devices, experiment design, and the relationship between SA and eye-tracking. This review summarizes the main metrics and key findings for each article and reveals a wide relationship between the eye-tracking metrics and SA. The influence of appropriately calibrated equipment, refined data collection protocols, and adequate selection of the eye-tracking metrics was examined. Further reviews are needed to systematically collect more evidence.

Benefits and Drivers of Green Building Projects Implementation in Nigeria

This study examined the benefits and drivers of implementing green building projects in Nigerian. Data was obtained through the distribution of questionnaire to professionals among the selected construction companies. Descriptive statistics and RII were utilized for the data analysis. Findings from the study revealed a high awareness of GB benefits and drivers among various construction participants in the industry. Among the environmental, economic, and social benefits revealed in this study include: reduced greenhouse gas emission, reduced energy consumption, low operating cost, high property value, knowledge transfers and community improvement, etc. Therefore, the study concluded that the most effect approach to holistic adoption of this panacea of global imbalance and degradation is the involvement of government in promoting the campaign on GB while considering promulgating policies and financial motivations.

Road Hypnosis Detection using Lane Detection

The study "Road Hypnosis Detection Using Lane Detection" focuses on the phenomenon known as "road hypnosis" or "highway hypnosis," a mental state in which a person can operate a vehicle for extended distances while reacting to outside events as expected, all without recalling that they did so consciously. Long stretches of time spent driving on smooth, featureless highways can cause this syndrome, which lowers awareness and causes a lack of stimulation. The study suggests a novel approach that makes use of lane identification algorithms to identify road hypnosis. It keeps track of the car's location in the lane and the driver's steering movements using sophisticated image processing and machine learning algorithms. Unusualities in these patterns, which could signal the start of road hypnosis, can be detected by the system. This system attempts to improve road safety and avoid accidents caused by decreased driver alertness due to road hypnosis by giving the driver real-time feedback or initiating automatic safety measures. The suggested approach is thoroughly tested and validated throughout the study in a variety of driving situations and conditions, proving its efficacy and suitability for incorporation into contemporary car safety systems. This study addresses a prevalent but frequently disregarded problem that might result in catastrophic accidents, making a substantial contribution to the field of driver safety systems. It also creates new opportunities for utilizing technology to raise driver awareness and enhance general traffic safety.

A Study on the Analysis of Law Violation Data for the Creation of Autonomous Vehicle Traffic Flow Evaluation Indicators

The study analyzes the reason for the steady increase in the number of traffic accidents in Yongin City, Gyeonggi-do, despite the improvement of Korea's traffic accident system and the decrease in the number of traffic accidents. Seven years of traffic accident data is collected to analyze major legal violations and accident risks and to derive accident-prone areas and road risk indices through QGIS mapping. Based on this analysis, it identifies the causes of accidents in multiple accident areas, proposes improvement measures like the installation of a median strip and an unmanned traffic control device near a crosswalk, and emphasizes the significance of driver awareness and safety education. The aim of the study is to analyze road risk factors and suggest improvement measures to prevent traffic accidents, with the goal of improving the traffic situation in Yongin city.

Evaluating Tunnel Users’ Literacy on Expressway Tunnel Fire Safety: Questionnaire Analysis and Policy Decisions

The number of tunnel fire accidents has increased with the scale of expressway tunnel construction and traffic flow. Due to the severity of tunnel fires, improving tunnel fire safety and reducing fire accident hazards has become a societal concern. It is essential to explore and evaluate tunnel fire safety literacy among the population. In this study, an online and on-site questionnaire survey was conducted in Hunan Province, China. A total of 1990 questionnaires were collected, of which 1573 were valid. Overall statistical analysis, descriptive statistics, and correlation analysis were performed on valid questionnaires. The results show that the overall level score rate of awareness of drivers and passengers regarding tunnel fire safety was only 0.43. In total, 58.42% of people were unaware of the pedestrian cross passages in expressway tunnels, and 68.40% were unable to recognize them. Similarly, 46.47% of people were unable to recognize evacuation signs in expressway tunnels. In addition, 39.29% of people chose the wrong evacuation behavior. The percentage of people who were aware of the correct usage of firefighting facilities in expressway tunnels was below 50.00%. Correlation analysis results show that tunnel users’ demographic characteristics significantly affected their cognition of expressway tunnel escape methods. This study shows that tunnel users’ emergency escape knowledge regarding tunnels is relatively low. Corresponding countermeasures were proposed to guide policy decisions for enhancing tunnel fire safety.

A safety-oriented framework for sound event detection in driving scenarios

The safety of drivers has become increasingly important in today's rapidly evolving transportation landscape, especially with the rise of autonomous and smart vehicles. This paper proposes a safety-oriented framework for sound event detection in smart vehicles using deep learning models. The goal of this framework is to increase driver awareness, prevent accidents, and provide acoustic forensic analysis. To achieve this, a meaningful taxonomy of event classes in a driving scenario is introduced, taking into account the event classes that are known to be related to major driving distractors. Based on this taxonomy, a dataset has been created to train and evaluate a fully-convolutional sound event detection model that was inspired by the well-known YOLO vision model. Experimental results demonstrated that the proposed model offers competitive results, outperforming a state-of-the-art baseline using recurrent connections. This comprehensive framework for sound event detection in smart vehicles aligns with the recommended directions for future mobility scenarios and has the potential to significantly improve the safety and performance of smart vehicles.

Smart Turns: Revolutionizing Road Safety

Abstract: A technological replacement for traditional convex mirrors at blind curves and T-junctions is presented in the abstract of this research article. The suggested system makes use of ultrasonic proximity sensors to see oncoming cars and sends timely notifications to drivers on both sides of the turn. The technology also computes and shows the oncoming vehicle's speed. A typical signal system or direct roadside projection of the alerts are both options provided by the system. This approach facilitates safer navigation at blind bends and T-junctions by using proximity sensors to measure vehicle speed and improve driver awareness. Our method improves accuracy, range, and reliability in vehicle detection and alerting by overcoming the limitations of convex mirrors. This technology improves safer driving by enhancing driver awareness and reducing accidents.

Mammal Roadkills in Lithuanian Urban Areas: A 15-Year Study.

We investigated roadkills in urban areas in Lithuania from 2007 to 2022, including two periods with COVID-19 restrictions on people's movement. We analyzed the proportions of wild and domestic animals in roadkill, annual trends, the predominant species involved, and monthly changes during the restrictions. Urban roads were characterized by a low species diversity of roadkilled mammals, with roe deer (Capreolus capreolus) dominating. Total numbers increased exponentially during the study period. The proportion of domestic animals, 12.2%, significantly exceeded that on non-urban roads in the country. The proportion of domestic animals decreased from over 40% in 2007-2009 to 3.7-5.4% in 2020-2022, while the proportion of wild mammals increased from 36.1-39.6% to 89.9-90.6%, respectively. During the periods of COVID-19 restrictions, the number of roadkills in urban areas was significantly higher than expected based on long-term trends. Compared to 2019, the number of roadkilled roe deer in 2020-2021 almost doubled from 700 to 1281-1325 individuals. These anthropause effects were, however, temporary. The imbalance between the roadkill number and transport intensity might require new mitigation strategies to sustain mammal populations in urban areas, at least through improving driver awareness on the issue.

Implementation of a psychomotor vigilance test to investigate the effects of driving fatigue on oil and gas truck drivers' performance.

Driving fatigue has been shown to increase the risk of accidents and potentially fatal crashes. Fatigue is a serious risk that some drivers do not take seriously. Previous studies investigated the effects of driving fatigue in the Malaysian oil and gas transportation industry by employing survey questionnaires. However, they did not explain the behavior of fatigue. Besides, these results required validation by a more reliable method that can describe how fatigue occurs. Thus, in this study, we used the Psychomotor Vigilance Test (PVT-192) and a short survey to address driving fatigue behavior and identify the influences of driving fatigue on driving performance in real life (on the road) with actual oil and gas tanker drivers. The total participants in the experimental study were 58 drivers. For the analysis, a Wilcoxon Signed Ranks Test, Z value and Spearman's rho were used to measure the significant difference between the pre and post-tests of PVT and the correlation between the fatigue variables and driving performance. During the experiment's first and second days, this study's results indicated that driving fatigue gradually escalated. Likewise, there was a negative correlation based on the test of the relationship between the PVT data and the driving performance survey data. Additionally, the drivers suffer from accumulative fatigue, which requires more effort from the transportation company management to promote the drivers awareness of fatigue consequences.

Exploring the differences in rider injury severity in vehicle-two-wheelers accidents with dissimilar fault parties

Objective: The division of responsibility in vehicle-two-wheelers accidents reflects the extent to which different fault parties contributed to the occurrence of the accident, with significant differences in the injuries sustained by the riders in accidents where diverse parties were primarily responsible. We want to explore the difference in the severity of injury of riders in different fault parties of accidents so that we can make targeted protection improvements. Methods: In this study, three generalized ordered logit models were established for the total sample (n = 1204), the sample with drivers as the primary fault party (n = 607), and the sample with riders as the primary fault party (n = 597), respectively, to explore the differential impact factors on rider injury severity in vehicle-two-wheelers accidents involving different fault parties. Inter-group difference tests were conducted on the mean rider injury severity caused by differential factors in different accidents. Combining the impact effect trends and mean differences in the model, the differences in rider injury severity in accidents involving different fault parties were analyzed from the standpoints of human, vehicle, and road factors. Results: It was found that the effects of curve on injury severity was sheerly opposite in accidents with different fault parties and that factors, such as visual obstruction, road surface condition, gender, and helmet wearing differed in their effects on rider injury severity under different fault parties accidents. This reveals the driving tendencies and states of both parties in different environments. Conclusion: Based on the differential impact factor analysis and rider injury characteristics in accidents involving different fault parties, suggestions for improvement were made from the perspectives of road facilities, and safety awareness of drivers and riders, which are beneficial for improving rider safety and providing a theoretical reference for future regulations on liability allocation.