Improve Road Safety Research Articles

Distributed Learning in Intelligent Transportation Systems: A Survey

The development of artificial intelligence (AI) and self-driving technology is expected to enhance intelligent transportation systems (ITSs) by improving road safety and mobility, increasing traffic flow, and reducing vehicle emissions in the near future. In an ITS, each autonomous vehicle acts as a node with its own local machine learning models, which can be updated using locally collected data. However, for autonomous vehicles to learn effective models, they must be able to learn from data sources provided by other vehicles and infrastructure, utilizing innovative learning methods to adapt to various autonomous driving scenarios. Distributed learning plays a crucial role in implementing these learning tasks in an ITS. This review provides a systematic overview of distributed learning in the field of ITSs. Within an ITS, vehicles can engage in distributed learning by interacting with peers through opportunistic encounters and clustering. This study examines the challenges associated with distributed learning, focusing on issues related to privacy and security in data intelligence sharing, communication quality and speed, and trust. Through a thorough analysis of these challenges, this study presents potential research avenues to address these issues, including the utilization of incentive mechanisms that rely on reputation, the adoption of rapid convergence techniques, and the integration of opportunistic federated learning with blockchain technology.

Performance Analysis of Routing Protocols for Urban VANET Based on SUMO and NS2.35

To make the dream of intelligent transportation systems come true, the development of the autonomous cars is considered as a tremendous disruptive innovation in the coming years. Vehicular ad hoc networks (VANETs) are being used as a tool for improving road safety by alarming the drivers about accidents occurred ahead of them or for providing internet access via gateways along the road. Due to highly dynamic nature of nodes in VANETs, designing a routing protocol for VANET is quite challenging compared to other networks environment. Researchers have suggested several routing algorithms for VANETs. Few routing algorithms are based on topology based whereas others have considered position based etc. As no standard scheme is available for choosing a routing protocol in VANETs, this paper gives an insight on how to choose a routing protocol depends on varying condition of traffic such as number of nodes on the roads and maximum speed of nodes. The popular protocols of Topology Based Routings and Position Based Routing have been chosen for analysis on varying traffic environment. All the protocols have been critically tested for different metrics such as packet delivery ratio and average end-to-end delay during the simulation. Simulation is carried out with the help of SUMO traffic simulator and open-source simulation tools NS2.35 network simulator. Simulation result show that Position Based Routing is performing better at low average end-to-end delay than Topology Based Routings, but Position Based Routing has the lowest possible performance in packet delivery ratio than Topology Based Routings.

Future-Proofing Road Safety: Adapting ISO 26262 for Advanced V2X Integration

This study investigates the integration of ISO 26262 with Vehicle-to-Everything (V2X) communication technologies. Emphasize the necessity of expanding this safety standard to include vehicle-to-infrastructure integration and addressing additional safety measures to accommodate current vehicles’ complex and rapidly evolving networks. The study uses an extensive review approach, which considers existing safety pro-tocols and a qualitative literature synthesis to evaluate current standards and anticipate future requirements from them. It also looks at developments in V2X communication technology while identifying gaps within the present scope of ISO 26262 concerning autonomous and connected cars. The findings indicate a significant improvement in road safety plus efficiency in traffic management by integrating ISO 26262 with V2X communication. Further, the research points out areas such as security lapses, real-time data processing deficiencies, interoperability hitches, and scalability limits. These findings call for amendments to current safety standards so that they can take care of those problems. Failure to include V2X into ISO 26262 may com-promise reliability altogether, hence posing a grave danger to future intelligent transportation systems (ITS)’s dependability. The research concludes that integrating them will help lower risks brought about by the dynamic nature of vehicles’ environment, thereby facilitating the establishment of globally solid and safe systems for communicating between vehicles.

Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.

Occupational Stress and Its Effect on Health Status Among Karnataka State Road Transport Corporation (KSRTC) Bus Drivers: A Cross-Sectional Study.

Occupational stress is a critical health concern across various professions, often linked to adverse health outcomes and increased risk-taking behaviors. Bus drivers are especially vulnerable due to prolonged shifts, exposure to environmental pollutants, and higher rates of sickness absenteeism. This study aimed to assess the prevalence of occupational stress among Karnataka State Road Transport Corporation (KSRTC) bus drivers and the factors contributed. In addition, it explores the relationship between job-related stress and overall health status, providing insights into the occupational challenges faced by this workforce. A cross-sectional study was conducted over two years from June 1, 2022, to June 30, 2024, at the KSRTC depot in Kolar, serving urban and rural areas. A representative sample of 235 bus drivers was selected from a total of 654 using simple random sampling. Socio-demographic details, including age, gender, education, religion, family type, and per capita income, were collected using a pre-tested semi-structured self-administered questionnaire. Stress levels were assessed using the American Institute of Stress (AIS) questionnaire, a widely used tool for evaluating various stress dimensions. Data were collected through interviews, entered into Excel, and subsequently analyzed using IBM SPSS Statistics for Windows, Version 22 (Released 2013; IBM Corp., Armonk, New York, United States). The study revealed that 65% of KSRTC bus drivers experienced moderate to severe stress. Significant factors associated with occupational stress included urban residency, divorce, chronic alcohol consumption, current tobacco chewing, hypertension, higher BMI, long working hours, and high waist-hip ratio. Binary logistic regression showed urban residents had higher stress odds than rural residents (OR=1.27, p=0.03). Divorced individuals and chronic alcoholics also had higher odds of stress (OR=1.24, p=0.05 and OR=1.33, p=0.04, respectively). Stress was significantly associated with hypertension (OR=1.40, p=0.01), obesity (OR=1.30, p=0.03), morbid obesity (OR=1.47, p=0.01), high-risk waist-hip ratios (OR=1.42, p=0.01), and longer working hours (>12 hours, OR=1.30, p=0.03). Health assessments revealed a high prevalence of hypertension (39%) and obesity (65%). The study highlights a high prevalence of occupational stress among KSRTC bus drivers in Kolar, with 65% experiencing moderate to severe stress. Factors such as urban residency, lifestyle habits, and physiological parameters significantly contribute to stress. These findings underscore the need for targeted health promotion programs and policy changes to enhance the health of essential workers and improve road safety.

Evaluation of the effectiveness of road safety measures and their impact on the pattern and distribution of extracranial and intracranial hemorrhages in fatal head injury cases in Uttar Pradesh, India

Introduction: In Uttar Pradesh, India, there are many fatal head injuries as a result of road traffic accidents (RTAs). Studying the pattern and distribution of intracranial hemorrhages, a frequent complication of severe head trauma might provide vital information on the efficacy of traffic safety regulations. To improve road safety tactics and lower fatal head injuries in Uttar Pradesh, this study intends to assess the effect of road safety measures on the frequency and distribution of intracranial hemorrhages in fatal head injury patients. Aim: This study’s objective is to assess the influence of current road safety initiatives on intracranial bleeding distribution and patterns in fatal head injury cases in Uttar Pradesh. Methodology: The 604 RTA cases with head injuries that were brought to the mortuary of the S.R.N. Hospital in Prayagraj for a medical-legal postmortem assessment over the course of a year, from April 2021 to March 2022, make up the entirety of this prospective study. Following receiving institutional ethical approval and informed consent, structured interviews with attendees utilizing a questionnaire were done to gather the data. Result: The results show that certain traffic safety measures have reduced the frequency of fatal head injuries in RTAs. A decreased incidence of intracranial hemorrhages was linked to improved traffic infrastructure, including well-planned junctions, pedestrian crossings, and distinct bicycle lanes. Similar results were shown in serious head injuries when traffic restrictions including speed limits, seat belt use, and helmet legislation were strictly enforced. Conclusion: There are still issues with fatal brain injuries from car accidents, despite some encouraging results. Safety legislation violations, a lack of public knowledge, and inadequate enforcement all contribute to the issue. Disadvantaged groups like walkers and users on two-wheelers continue to be especially susceptible. The results of this investigation offer important new understandings of the efficiency of traffic safety measures and their influence on the distribution and pattern of cerebral hemorrhages in Uttar Pradesh, India. The results point to the necessity of improved public education efforts and traffic safety laws. More research and focused interventions are required to address specific risk factors among various road user groups. This will result in a safer driving environment and a decline in catastrophic brain injuries.

A Survey of Resource Allocation in V2V Communications

In this paper, a comprehensive analysis of resource allocation in vehicle-to-vehicle (V2V) communication systems is presented, which is crucial for enhancing traffic control and improving road safety and driving enjoyment. A variety of resource allocation strategies are classified and examined in this paper, including game theory-based approaches, spectrum allocation, energy allocation, location-based allocation, and machine learning-based procedures. Open research questions and areas that need further study in the field of V2V communication are highlighted for future benefit. This survey helps readers make informed decisions about system design, implementation, and improvement by providing a comprehensive overview of the latest developments in V2V resource allocation. It also indicates areas that need further research and possible directions to enhance the security, flexibility, and scalability of V2V communication networks.

Gender Differences in Prefrontal Cortex Response to Negative Emotional Stimuli in Drivers.

Road safety improvement is a governmental priority due to driver-caused accidents. Driving style variation affects safety, with emotional regulation being pivotal. However, functional near-infrared spectroscopy (fNIRS) studies show inconsistent prefrontal cortex activity during emotion processing. This study examines prefrontal cortex response to negative emotional stimuli, particularly traffic accident images, across drivers diverse in age and gender. The study involved 118 healthy males (44.38 ± 12.98 years) and 84 females (38.89 ± 10.60 years). The Multidimensional Driving Style Inventory (MDSI) was used to assess driving behavior alongside fNIRS recordings. Participants viewed traffic accident and neutral images while prefrontal oxygenation was monitored. Women rated traffic accidents (t-test = 2.43; p < 0.016) and neutral images (t-test = 2.19; p < 0.030) lower in valence than men. Arousal differences were significant for traffic accident images (t-test = -3.06; p < 0.002). correlational analysis found an inverse relationship between Dissociative scale scores and oxygenation (all p-values ≤ 0.013). Greater prefrontal oxygenation occurred with neutral images compared to traffic accidents. Left hemisphere differences (t-test = 3.23; p < 0.001) exceeded right hemisphere differences (t-test = 2.46; p < 0.015). Subgroup analysis showed male participants to be driving these disparities. Among adaptive drivers, significant oxygenation differences between neutral and accident images were evident in both hemispheres (left: t-test = 2.72, p < 0.009; right: t-test = 2.22, p < 0.030). Male drivers with maladaptive driving styles, particularly dissociative ones, exhibit reduced prefrontal oxygenation when exposed to neutral and traffic accident images. This response was absent in female drivers, with no notable age-related differences.

Real-time rear-end conflict prediction on congested highways sections using trajectory data

Predicting rear-end conflicts in advance can avoid potential crashes and significantly improve road safety, especially in congested road sections. Many existing studies adopt macroscopic aggregated traffic flow state features and or environment features for rear-end conflicts prediction, which seems to overlook the impact of the temporal trends of various features during the conflict process on the outcomes. Thus, this paper uses microscopic trajectory data of front and rear vehicles for conflict prediction and explored the impact of trajectory changes trend on conflicts formation. A Gated Recurrent Unit (GRU) is employed to learn and encode conflict and non-conflict trajectory data and perform binary classification. The model has a 93 % recall and a 1.41 % false alarm rate. The Local Interpretable Model-agnostic Explanations (LIME) tool also explains the relationships between predicted conflict probability and input microscopic trajectory data. From the time analysis of the input trajectory using LIME, the following conclusions can be drawn. In congested road segments, when the speed of the leading vehicle is below 3 m/s and the speed of the following vehicle is above 4 m/s, it has a significant positive effect on the occurrence of conflicts. And some aggressive acceleration behaviors of drivers have the positive effect also. In addition, the reasons for conflicts among most vehicles are identical Because their feature distributions are similar. These findings can provide targeted insights for the management of ATM in congested road segments.

Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks

This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdependencies among various risk factors and their cumulative impact on road safety are investigated. Our findings show that two combined risk factors have different effects on risk level when considered individually. Two case studies related to human state risk factors and environmental risk factors, such as driving under the influence and snowy roads, as well as fatigue and snowy roads, have an amplified effect on the risk level. The findings highlight the importance of considering nonlinear interactions among risk factors when developing effective and targeted strategies for accident prevention and road safety improvement. This research contributes to the field of road safety by presenting a new methodology for understanding and mitigating road safety hazards.

Enhancing work zone Safety: A psychological and behavioral analysis of Anti-Tailgating strategies using dynamic message signs and fixed warning signs

Rear-end collisions in work zones, particularly those resulting from tailgating, pose a significant safety challenge that has not been adequately addressed in road safety research. Existing studies have extensively investigated speeding, but the psychological triggers and behavioral patterns leading to tailgating remain largely unexplored. Bridging this critical gap, this research introduces an anti-tailgating strategy using dynamic and fixed message signs, designed with a deep understanding of driver psychology. The methodology involved a user-centric survey assessing the effect of positively toned, non-numerical anti-tailgating messages, which favored clearer comprehension and elicited favorable psychological responses from drivers. Field experiments demonstrated that these signs effectively increased headway and decreased tailgating, without the traffic disruptions associated with pavement markings or the technological limitations of in-vehicle alerts. These encouraging results underscore the potential of strategically placed message signs to positively influence driver behavior. This research not only fills a significant void by elucidating the behavioral factors of tailgating in work zones, but also paves the way for the implementation of behaviorally informed and nonintrusive road safety interventions. The use of dynamic and fixed message signs emerges as a promising, technologically inclusive, and psychologically effective tool for improving road safety.

Machine Learning for Prediction of Driver Takeover Time in Automated Driving: Insights from Non-urgent Low Consequence Scenarios

This work investigates driver takeover times in non-urgent, low consequence scenarios within conditionally automated driving. Using physiological and behavioral data from 46 participants in a driving simulator, classification algorithms were applied to predict metrics of takeover time following a takeover request (TOR). Eye-tracking, heart rate variability, and computer-vision based body posture features were analyzed for their predictive power. The Naïve Bayes algorithm outperformed other models, achieving an accuracy of 78% when predicting the time to first gaze in the driving scene following a TOR. Results from feature selection showed eye-tracking metrics to have the most predictive power. These results suggest that eye-tracking metrics and simple, computationally efficient, 2-class algorithms may be sufficient for predicting takeover time in non-urgent, low-consequence scenarios. This research provides evidence for integration of physiological sensing into adaptive automated driving systems (ADS) to develop context-aware TOR alert systems to improve road safety.

Data safety prediction using YOLOv7+G3HN for traffic roads

Pulau Pinang has introduced several measures to enhance traffic safety and promote sustainability, including the installation of CCTV systems and the implementation of smart solutions and green technology as part of the Penang 2030 vision, aligning with the Sustainable Development Goals (SDGs). However, despite these efforts, road accidents persist due to non-optimised detection models, incomplete data from manual reporting, and technological constraints in real-time video analysis and predictive modelling. This study evaluates the effectiveness of the YOLOv7+G3HN framework for vehicle detection and near-miss analysis, with a focus on the influence of video quality on detection performance. The research aims to understand how high- and low-quality video inputs affect the accuracy and computational efficiency of detection algorithms. High[1]quality videos resulted in significantly faster computation times for vehicle detection than low-quality videos, highlighting the importance of video resolution in optimising detection processes. Despite the robustness of the algorithm, with no errors detected in both video qualities, higher miss detection rates in low-quality videos suggest that lower resolution may compromise detection accuracy and the effectiveness of monitoring systems. Near-miss analysis revealed that high-quality videos had a lower probability of near-miss occurrences than low-quality videos, highlighting the importance of video resolution for detection efficacy. These findings emphasise the critical role of high-resolution video inputs in enhancing detection accuracy and reliability, advocating for their implementation to optimise vehicle detection and improve road safety. Additionally, YOLOv7+G3HN outperforms YOLOv7 in both accuracy and speed. The study concludes that the YOLOv7+G3HN framework is effective for vehicle detection and near-miss analysis, provided that video quality is considered in system design and implementation.

Wait or Pass? Promoting intersection’s cooperation via identifying vehicle’s social behavior

Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers’ decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants’ final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction.

Examining the effectiveness of an education-based road safety intervention and the design and delivery mechanisms that promote road safety in young people

BackgroundRoad traffic collisions (RTCs) are the leading cause of deaths for young people worldwide. Whilst educational interventions that adopt fear-based messaging are commonly used to improve road safety in young people, limited focus has been directed to examining how they are delivered. Accordingly, this mixed-method study aims to i) measure the effectiveness of a UK intervention called Safe Drive Stay Alive (SDSA) for improving road safety knowledge, attitudes and intentional behaviors, and ii) what design and delivery mechanisms are important for achieving this. MethodsIn study one, participants completed online questionnaires that measured road safety knowledge, attitudes, and behavioural intentions pre- (n = 1304) and post-intervention (n = 407), and two months later (n = 72). In study two, focus groups were conducted with 10 young people post-intervention to understand what mechanisms were important for promoting road safety. ResultsStatistical analysis of questionnaires showed that road safety knowledge, attitudes, and intentional behaviours significantly improved post-intervention. However, descriptive comparisons of the smaller number of two-month follow-up questionnaires indicated that ratings returned to pre-intervention levels. Thematic analysis of focus groups highlighted that having emotive, realistic, and relatable content was important for encouraging young people to attend to the message. However, more focus was needed on how to address peer pressure. ConclusionSDSA improves road safety knowledge, attitudes, and behavioural intentions. However, there are indications that benefits may be short lived, requiring ‘top up’ interventions. Whilst young people also perceive SDSA to be useful, engaging, and delivering an important message, support is needed for addressing peer pressure.

Perancangan Sistem Deteksi Tingkat Kemiringan Jalan Sederhana Dengan Metode Otsu Thresholding Menggunakan Colab

This research focuses on the development of a simple system for detecting road slopes using the Otsu Thresholding method. The primary objective is to create an effective and efficient system capable of identifying different road slope levels accurately. The system utilizes image processing techniques, where the Otsu Thresholding method is applied to differentiate between road surfaces and surrounding environments. By analyzing these images, the system determines the degree of road slope. This study emphasizes the importance of accurate road slope detection for improving road safety and maintenance. The observation method was employed in this research to gather relevant data and evaluate the system's performance. The results demonstrate that the designed system is capable of effectively detecting various road slope levels, providing a valuable tool for transportation infrastructure monitoring.

A Highway Monitoring Necessity Grading Method Considering Traffic State Stability

In order to meet the basic requirements of expressway operation and maintenance management in the new era,refine the monitoring requirements of different expressway sections,and maximize the operation benefit of expressway monitoring system,this paper puts forward the stability of traffic state related to overtaking frequency,speed difference and traffic fluctuation at the entrance and exit,and obtains the grading detection method of expressway through entropy method and global Moran index,which provides a basis for optimizing monitoring and improving road safety.

The contribution of excessive or inappropriate speeds to road traffic crashes and fatalities: A review of literature

Road traffic crashes and fatalities pose a significant global challenge, particularly in low- and middle-income countries (LMICs), where a major cause is consistently linked to speeding (i.e., excessive or inappropriate speeds). However, the existing body of evidence regarding the estimated contribution of speeding to crashes and fatalities, remains limited and is considered outdated. This paper bridges this knowledge gap by reviewing evidence on the contribution of speeding to crashes and fatalities. The review draws on a wide range of sources including peer-reviewed studies on the subject, road safety monitoring-reports and available data summaries (104 sources). Data reliability was confirmed by including studies based on or linked to primary sources like police records, and excluding those with poor quality, implausible results, or lacked references to primary sources. The included sources contained 37 estimates from high-income countries (HICs), and 67 estimates from LMICs. Globally, HIC and LMIC estimates of contribution were calculated by assigning weights based on the proportion of fatalities in each country under this study. The results indicated that speeding contributes to approximately 54 % of fatalities worldwide, 57 % in LMICs, and 28 % in HICs. This translates to a speeding-related death every 49 s, with a 95 % likelihood of occurring in LMICs. These findings carry significant implications for policymakers emphasizing the urgent need to prioritize interventions that reduce speeding and improve road safety. Investigating gaps in LMICs data sources is a critical priority. In-depth studies and speeding intervention evaluations will enhance our current understanding of speeding contribution to crashes and fatalities.

Road car image target detection and recognition based on YOLOv8 deep learning algorithm

In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.

Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.

In this manuscript, we delve into the realm of lattice ordered complex linear diophantine fuzzy soft set, which constitutes an invaluable extension to the existing Fuzzy set theories. Within this exploration, we investigate basic operations such as and , together with their properties and theorems. This manuscript is more amenable in two ways, i.e., it enables real-life problems involving parametrization tool and applications with an existing order between the components of the parameter set based on the preference in the complex frame of reference. Adaptive cruise control (ACC) is a system designed for maintaining distance between two vehicles and to sustain a manually provided input speed. The purpose of cars with ACC is to avoid a collision that frequently happens nowadays, thereby improving road safety regulations amidst rising collision rates. The fundamental aim of this manuscript is to prefer an applicable car with ACC together with its latest model by defining a peculiar postulation of lattice ordered complex linear diophantine fuzzy soft set . Emphasizing real-life applicability, we illustrate the effectiveness and validity of our suggested methodology in tackling current automotive safety concerns, providing useful guidance on reducing challenges related to contemporary driving conditions.