Enhance Road Safety Research Articles

VIRTUAL POTHOLE DETECTION SYSTEM

The intersection of artificial intelligence and vehicular technology has birthed a realm of innovation, revolutionizing our engagement with the automotive sphere. This survey delves into the realm of virtual pothole detection systems, a pioneering endeavor aimed at mitigating road hazards through technological intervention. As society gravitates towards a digitalized landscape, the imperative for enhanced road safety looms large. Virtual pothole detection systems represent a concerted effort to address this pressing concern, leveraging cutting-edge advancements in sensor technology, data analytics, and vehicular communication protocols. This paper scrutinizes the evolution, methodologies, and efficacy of virtual pothole detection systems, offering insights into their operational paradigms and prospects. Amidst the backdrop of burgeoning urbanization and escalating vehicular traffic, the significance of such systems cannot be overstated. By elucidating the intricacies of virtual pothole detection systems, this survey endeavors to catalyze further research and innovation in the pursuit of safer, more resilient road networks Keywords: Virtual Pothole Detection System (VPDS), Sensor Technology, Data Analytics, Vehicular Communication, Road Safety, Pothole Detection, Accident Prevention, Road Maintenance, Artificial Intelligence.

The Role of Parenting Behaviors and Their Influence on Adolescent Drunk and Drugged Driving: 2016-2019, USA.

Drugged driving, the act of driving a vehicle under the influence of illicit drugs, by adolescents is a serious public health concern. Many factors contribute to this risk behavior, but much less is known regarding the role of parenting behaviors in this phenomenon. The purpose of this study was to examine specific parenting behaviors and their influence among a nationally representative sample of adolescents. Pooled data from the 2016-2019 National Survey on Drug Use and Health (NSDUH) among 17,520 adolescents ages 16-17 years old were analyzed. Differences were found in specific parenting behaviors and adolescent drugged/drunk driving, with parents not checking homework and not telling their children they are proud of them being the most influential. Findings from the present study may inform drugged driving prevention programs for parents and adolescents and enhance road safety interventions.

Enhancing Road Safety Vehicle Black Box with Advanced Accident Detection and Alert System

Autonomous vehicles want reliable and strong sensor suites and alert systems. This paper discusses the composition and performance of a sophisticated monitoring and alert system for automobile vehicle parameters. The number of automobiles has also grown quickly to meet the enormous population. Additionally, this resulted in an increase in accidents. The accident prevention strategies now in use are all static and dated. Additionally, there is no reliable accident detection system. Automobile vehicle parameters are continuously monitored by a microcontroller which stores the data logs containing vehicle parameter data into a sheltered digital memory card and in the cloud storage. The system doesn't solely record the vehicle parameters data of the automobile periodically, but also actively monitors for any sudden vehicle accident detection. The sensor may facilitate folks to analyze the accident quickly and lawfully when a collision happens to alert the emergency services to that location. The system will update the information whenever an abnormal system event happens. A black box in a vehicle gather driving information about the vehicle before, during and after a crash

Dynamic Perception-Based Vehicle Trajectory Prediction Using a Memory-Enhanced Spatio-Temporal Graph Network

In the realm of intelligent transportation systems, accurately predicting vehicle trajectories is paramount for enhancing road safety and optimizing traffic flow management. Addressing the impacts of complex traffic environments and efficiently modeling the diverse behaviors of vehicles are the key challenges at present. To achieve precise prediction of vehicle trajectories, it is essential to fully consider the dynamic changes in traffic conditions and the long-term dependencies of time-series data. In response to these challenges, we propose the Memory-Enhanced Spatio-Temporal Graph Network (MESTGN), an innovative model that integrates a Spatio-Temporal Graph Convolutional Network (STGCN) with an attention-enhanced Long Short-Term Memory (LSTM)-based sequence to sequence (Seq2Seq) encoder–decoder structure. MESTGN utilizes STGCN to capture the complex spatial dependencies between vehicles and reflects the interactions within the traffic network through road traffic data and network topology, which significantly influences trajectory prediction. Additionally, the model focuses on historical vehicle trajectory data points using an attention-weighted mechanism under a traditional LSTM prediction architecture, calculating the importance of critical trajectory points. Finally, our experiments conducted on the urban traffic dataset ApolloSpace validate the effectiveness of our proposed model. We demonstrate that MESTGN shows a significant performance improvement in vehicle trajectory prediction compared with existing mainstream models, thereby confirming its increased prediction accuracy.

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

AI-Driven Approach for Automated Real-Time Pothole Detection, Localization, and Area Estimation

Given the potential hazards and risks that potholes pose to road users, this study introduces an image-based system that utilizes a combination of a camera and GPS for real-time detection, georeferencing, and area estimation of potholes. The captured system data is processed in real-time using YOLOv8, a deep learning model proficient in object detection and segmentation. To enhance the precision and reduce the occurrence of false detections, the system is specifically trained to detect potholes, manholes, and patches. Additionally, the camera is calibrated to accurately estimate the area of identified potholes. The proposed system achieved a mean average precision of 91% in detecting potholes, 98% in detecting manholes, and 90% for detecting patches. A salient feature of this system is its capability to localize potholes with reference to pavement line lane markings. This ability could facilitate proactive lane closure planning by maintenance crews, further enhancing road safety measures. The study findings suggest that the system holds significant potential for practical implementation. Its deployment could assist transportation agencies in the prioritization of road repairs, resource allocation, and advance planning for lane closures, ultimately enhancing the efficiency of their maintenance workflows.

Investigating the feasibility of dynamic speed limit on Indian highways

Speed limits that change as per real time traffic, road, and weather conditions are termed as Dynamic Speed Limits (DSL). Road users are informed of speed limit changes in DSL scheme by electronic signs or Variable Message Signs (VMS) placed above the lanes or side-mounted along highways. In India, most of the highways have static speed limits. The speed limits are kept lower at locations or stretches adjacent to roadside schools, market areas, bus stops and intersections, for enhancing safety of Vulnerable Road Users (VRUs). This causes higher travel time to through traffic while travelling through these stretches. DSL has the potential in reducing the travel time and the variability of speed of vehicles, thus helps in enhancing road safety. The present work investigates the feasibility of DSL on various segments of a typical Indian highway passing through various roadside developments and human activities using cost-benefit analysis. The travel time benefits due to DSL are estimated by comparing the travel times with static and dynamic speed limit conditions. The results indicate that there is a significant amount of saving in aggregate travel time of all vehicles passing through the stretch due to the application of DSL. The benefit cost ratios greater than unity indicated that it is beneficial to deploy DSL on several segments of the highway passing through roadside schools, market areas, bus stops and intersections. The Benefit-Cost Ratio is also used advantageously to prioritize viable locations for deployment of DSL. The findings from the present work are likely to be of interest to policy makers in different developing countries where infrastructure/facilities for VRUs are deficient, road safety is a major concern and speeding is identified as a major problem.

Multi-agent system architecture for winter road maintenance: a real Spanish case study

Road safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

Vehicle Accident Alerting System

This journal presents a detailed exploration of the design, development, and implementation of a vehicle accident alerting system aimed at significantly enhancing road safety and improving emergency response to accidents. The system employs a multi-faceted approach, integrating advanced sensor technology, real-time data processing algorithms, and efficient communication protocols to detect accidents promptly and notify emergency services and relevant stakeholders. The core components of the system include sensors strategically installed within vehicles to monitor motion, orientation, and environmental conditions. These sensors provide raw data, which is processed in real-time using sophisticated algorithms to detect patterns indicative of potential accidents. The system incorporates confirmation mechanisms to reduce false positives and accurately assess the severity of detected events. Key Words: Accident detection, alert system, GPS, GSM, Accelerometer, Android application.

Analyzing traffic conflict scenarios and safety assessment in the context of intelligent vehicles: A research review

The several internal disputes and various types of traffic accidents at an urban crossroads make for a complex traffic scenario. Because accident data and crash-based methodologies are limited, researchers are now examining other safety measures, including traffic conflicts that do not depend on crashes. To better assess road safety in the decades to come new real-time data is essential. The automobile industry is heading toward intelligent cars with the goal of enhancing road safety. Although there is a common failure mechanism between accidents and traffic conflicts during driving, which makes it possible to apply crash frequency and severity estimates to traffic conflict frequency and severity models, predicting traffic conflicts presents distinct difficulties. This study discusses conceptual and methodological concerns while reviewing research papers on traffic disputes as safety measures. It offers a thorough synopsis of previous research from the viewpoint of intelligent vehicles. It explains that there are three primary categories into which the perception technology of intelligent cars may be separated: perception technology, communication technology, and the combination of perception and communication technologies. The paper emphasizes the need of harmonizing international safety standards as well as the role that governments and regulatory agencies play in guaranteeing the security of intelligent vehicles. It highlights the need for additional research on transport conflict modeling techniques and offers practical recommendations for future study.

Helmet Detection

Currently, there is a prevailing issue concerning the practice of riding motorcycles without helmets. Despite legislation mandating helmet usage for public safety reasons, there remains a significant number of individuals who disregard this requirement, leading to an alarming increase in accidents and fatalities. To mitigate this problem, we propose a method for the automated detection of helmetless motorcyclists through surveillance systems. Our approach utilizes the YOLO deep learning framework for detecting individuals without helmets, while a SM algorithm is employed recognition. This process involves several stages, including vehicle classification, pre-processing, and number plate identification, using images or videos captured by surveillance cameras. Implementing such a system is crucial for enhancing road safety and preventing accidents. By automatically identifying helmetless riders and extracting their vehicle numbers, law enforcement agencies can efficiently apprehend violators. This data can then be utilized to impose fines on repeat offenders, thereby reinforcing compliance with helmet regulations and reducing the occurrence of preventable accidents

Smart Application to Avoid Road Accidents Caused by Speed Breakers

The intelligent application presented in this study aims to stop speed breakers from causing accidents on the road. The application employs on board sensors and GPS technologies to identify speed violators and notify drivers. The software detects speed breakers and alerts drivers in real time by combining sensor data analysis and machine learning methods. Our application aims to enhance road safety by notifying drivers about speed breakers. Therefore, reducing the possibility of accidents and making the road safer for everyone

Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach

Enhancing Road Safety Decision-Making through Analysis of Youth Survey Data: A Descriptive Statistical Approach

Cumulative effect of driver’s historical violations on traffic accidents: New evidences from China

This study explores the impact of long-term traffic violations on accident risk, a frequently overlooked aspect in existing research. Analyzing violation data from Guilin, China, over a decade (2010-2020), including accident records from 2016-2020, the study categorizes drivers into three groups based on their cumulative violations. Given the challenges posed by high-dimensional data and the need for interpretability, the study employs a combination of CatBoost and SHAP models to assess factors influencing accident severity across these driver groups. Results indicate that for drivers without cumulative violations, road type, vehicle type, household registration, and age significantly influence accident risk. Among those with 1–18 cumulative violations, road type, cumulative penalty points, vehicle type, and cumulative fines emerge as significant factors. Drivers with more than 19 cumulative violations are affected by road type, vehicle type, driving experience, and terrain. Notably, penalty points are more effective than fines in reducing the risk of serious accidents, and fines prove significant only for those with fewer than 19 cumulative violations over a decade. In summary, the cumulative violation count effectively distinguishes driver groups, highlighting marked variations in accident risk factors. This research offers valuable insights for devising region-specific measures to enhance road safety.

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

BLACK BOX AND BLIND SPOT DETECTION SYSTEM IN AUTOMOBILES

This paper presents the development of an affordable Black Box and Blind Spot Detection System for automobiles, integrating various sensors and microcontrollers. The Black Box monitors crucial parameters such as speed, tilt, temperature, and alcohol presence, uploading data to a cloud-based Firebase database in real-time. The Blind Spot Detection System utilizes an ultrasonic sensor to alert drivers of objects in blind spots, aiming to prevent accidents. The system's user-friendly website allows easy data access and analysis, potentially enhancing road safety and facilitating crash investigations. Key words- Black box, Blind spot detection system, Firebase real time database.

EDT-STACK: A stacking ensemble-based decision trees algorithm for tire tread depth condition classification

Tires serve as the critical interface between a vehicle and the road. Their condition significantly impacts road safety, vehicle performance, fuel efficiency, and operational costs. Developing a machine learning model for accurately and efficiently classifying tire conditions using tread depth measurements poses a critical challenge in enhancing automotive safety and maintenance. This research aims to address this challenge by leveraging machine learning techniques to categorize and assess tire conditions based on tread depth, thereby contributing to developing a robust and automated system for tire safety condition monitoring in diverse vehicle fleets. This article presents a novel ensemble method-based machine learning algorithm called EDT-STACK (Stacking Ensembles-Based Decision Tree) that combines several decision tree-based methods using a stacking ensemble technique. The model is trained and tested on a dataset of 500 tires (new and disposed) with different tread depths, The model achieves 99 % accuracy in all classes, demonstrating its efficacy in classifying tire conditions from tread depth data. The study also compares the performance of EDT-STACK with other algorithms. Its superior performance outshines other decision tree algorithms, such as XGBoost, AdaBoost, Random Forest, ID3, and Histogram-based Decision Tree, which achieve accuracies of 98 %, 78 %, 85 %, 98 %, and 98 %, respectively. This study concludes that EDT-STACK offers an innovative solution for tire condition assessment, which can enhance road safety, reduce environmental impacts, and optimize vehicle maintenance.

Smart Vehicle Load Management with Arduino Sensors

In the context of road safety, vehicle overloading remains a persistent concern, posing a risk of accidents, infrastructure damage, and potential harm to human life. This project introduces an innovative solution for mitigating this issue through an Arduino-based smart vehicle load management The system incorporates load cell, Ultrasonic sensor, an LCD display, and a buzzer to effectively detect and prevent vehicle overloading, contributing to improved road safety. The smart vehicle load management with arduino sensors is designed to monitor the weight distribution on a vehicle using strategically placed load cells and detect variations in vehicle obstacle detection through Ultrasonic sensors. Upon detecting an overload condition, the system promptly triggers a response by stopping the motor, which prevents further loading, and immediately displays a warning on the LCD screen. Simultaneously, it activates an audible alert through the buzzer. This rapid reaction not only averts potential accidents resulting from overloading but also extends the vehicle's operational life by minimizing wear and tear. The Arduino-based Vehicle Overload Detection System represents a cost-effective and efficient approach to addressing the critical issue of vehicle overloading. Its monitoring capabilities, coupled with instant warnings and alerts, establish it as a valuable tool in enhancing road safety and reducing the risks associated with overloading.

Automatic Braking System

With the proliferation of vehicles and the integration of technology into daily transportation, ensuring road safety has become paramount. Traffic accidents, often resulting in substantial damage and casualties, persist as a global concern. The Automatic Braking System (ABS) stands as a pivotal safety innovation adopted by vehicle manufacturers worldwide. This paper explores the significance and functionality of ABS in preventing wheel lock-up during braking, thereby enabling drivers to maintain steering control. Through an examination of ABS technology, its effectiveness, limitations, and potential advancements, this research aims to contribute to the ongoing discourse on enhancing road safety measures in the modern automotive landscape.

Journey to Safety: Innovations and Challenges in Road Safety Management across Indian Highways

In India, the number of traffic accidents has significantly increased as a result of inadequate transportation infrastructure, appropriate roads, and effective government. India has the highest number of collisions worldwide each year and is among the least safest nations on the road. The purpose of the study is to evaluate the safety of the current road infrastructure and identify any potential risks or hazards. To assess the safety performance of interchanges, motorways, and other transportation facilities and pinpoint areas in need of improvement, comprehensive safety audits of the infrastructures included in the study are required. Through this study, stakeholders may enhance road safety for all users by gaining a better understanding of the safety risks associated with the current road infrastructure and the capacity to make data-driven decisions. The primary goals of this study are to provide a thorough assessment of the security of the road network, identify potential hazards, and develop recommendations