A Review Paper on Automobile Black Box System for Accident Analysation

International Journal of Research Publication and Reviews, Vol 6, Issue 9, pp 5605-5609, September, 2025 International Journal of Research Publication and Reviews Journal homepage: www.ijrpr.com ISSN 2582-7421 A Review Paper on Automobile Black Box System for Accident Analysation 1Vedant Ghike, 2Sahil Ghuge, 3Dhruv Akhare, 4Kshitij Deshmukh, 5Roshan Shingare, 6Om Awaghad, 7R.M. Gharat. 1,2,3,4,5,6, Students, Electronics Engineering, 7HOD, Electronics Engineering. Dr. Panjabrao Deshmukh Polytechnic, Amravati, Maharashtra, India. ABSTRACT: Road traffic accidents are a leading cause of fatalities and injuries worldwide, with human error, alcohol impairment, and delayed emergency response being major contributors. Conventional accident reporting methods are often unreliable, while post-accident investigations face challenges due to insufficient data. This creates a strong need for intelligent systems capable of real-time accident detection, driver monitoring, and reliable data recording to improve road safety and aid accident analysis.This paper aims to enhance road safety through the development of an integrated real-time accident sensing and alcohol monitoring system. The proposed design employs accelerometers and gyroscopes to sense collisions, trigger warnings, and enable event data logging. A breathalyzer module monitors the driver’s blood alcohol concentration (BAC), and if levels exceed legal limits, the system immobilizes the vehicle’s ignition to prevent unsafe driving. A “black box” feature continuously logs critical parameters such as speed, GPS location, time, and environmental conditions before, during, and after a collision. Data from multiple sensors are processed via a microcontroller, integrated with a smartphone application for real-time monitoring and alert generation. While data are stored locally for immediate use, the system also supports cloud-based upload for advanced analysis, accident investigations, and insurance claim validation. Keywords – Raspberry pi 4, Automobile Black Box, Accident Analysis, Alcohol Sensor,GPS and GSM Module. 1. INTRODUCTION: Road accidents have become a major global concern, resulting in significant loss of life, property, and resources every year. According to the Global Status Report on Road Safety 2023 by the World Health Organization (WHO), approximately 1.19 million people die annually due to road traffic crashes, with millions more injured or disabled. Despite advances in automotive technologies, accident investigation and prevention remain major challenges. Traditional methods of accident analysis often rely on eyewitness accounts, police reports, and post-accident inspections, which can be unreliable or incomplete. [6] To address this gap, the concept of an Automobile Black Box System has emerged as a crucial solution. Much like the black box used in aircraft to record flight data, an automobile black box collects, stores, and transmits critical data related to the vehicle’s operation and surroundings before and during a crash. This data plays a vital role in accurately analysing the cause of accidents, enhancing vehicle safety systems, and enabling faster emergency response. The proposed system is powered by a Raspberry Pi 4 single-board computer, which serves as the central controller for all data acquisition, processing, and communication tasks. It is equipped with a range of integrated sensors and modules. An alcohol sensor monitors the driver’s breath to detect intoxication, helping prevent drunk-driving incidents. Door sensors track the opening and closing of vehicle doors, which is useful in determining the status of occupants at the time of an accident. Audio recording modules capture real-time sound within the vehicle, offering contextual insights such as driver alerts, mechanical failures, or external noise events. To pinpoint the exact location of an incident, the system uses a GPS module, while a GSM module allows automatic transmission of data and alerts to emergency contacts or authorities. The addition of two cameras— one facing the road and the other monitoring the cabin—ensures visual documentation of both external and internal environments during critical moments. Moreover, a fire-detection panel is integrated to sense any fire outbreaks postaccident, triggering alerts and possibly activating safety mechanisms. This review paper explores the design, components, working principles, and practical applications of the automobile black box system powered by the Raspberry Pi 4. It also discusses existing research, benefits, implementation challenges, and the potential of such systems to enhance road safety, assist legal investigations, and reduce response times in emergencies. International Journal of Research Publication and Reviews, Vol 6, Issue 9, pp 5605-5609 September, 2025 5606 2. LITERATURE REVIEW: Researchers have been working on different ideas to make automobile black-box systems safer and more useful. Tallapaneni Narendra et al.(2021) describe in their paper “Automobile Black Box System for Accident and Crime Analysis” a practical black-box unit for cars that records speed, GPS location and other sensor data, encrypts the information to prevent tampering, and automatically sends a text alert with the vehicle’s position whenever a crash is detected. [1] Aleesha Navas et al. (2024) present “IoTBased Accident Detection Systems – A Review,” which surveys many internet-of-things solutions that use accelerometers, cameras, and smartphone sensors to identify accidents, classify their severity, and quickly notify emergency services, showing how IoT can shorten rescue time and save lives [2] Roshik Naga Sai Patibandla et al.(2025) in “Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction” investigate how drivers feel about in-vehicle monitoring such as eye-tracking or passive breath-alcohol checks, finding that people like nonintrusive systems but remain concerned about privacy, data storage, and false alarms [3] Praful Raman et al. (2025) propose in “Car Accident & Alcohol Detector & Recorder Blackbox” an integrated safety device that joins a breath-alcohol sensor with accelerometers, gyroscopes, and GPS to detect crashes, block the engine if the driver is over the legal limit, store all preand post-accident data securely, and optionally back it up to the cloud for later investigation. [4] Patibandla et al. (2025) examined integrated safety monitoring systems that detect alcohol impairment and driver distraction. Their study highlighted that while non-intrusive systems such as eye-tracking are preferred, drivers expressed concerns about privacy, data handling, and system reliability. Trust in system accuracy strongly influenced user acceptance, suggesting that transparency and local data processing are essential for adoption[7] Monika et al. (2023) proposed a car black box system similar to aircraft recorders, designed to capture preand post-accident data using IoT-based sensors. The system incorporates GPS for location tracking and GSM for alerts, which assists emergency response teams and insurance investigations. The black box also monitors vehicle parameters such as braking and speed, contributing to accident analysis and prevention[8] Shubham et al. (2021) presented a comprehensive survey on IoT-based automatic accident detection approaches. The study compared methods including smartphone sensors, GSM/GPS modules, vehicular ad hoc networks (VANET), and machine learning algorithms. While these technologies improve detection speed and accuracy, issues such as network reliability, false alarms, and cost of deployment remain critical challenges[9] Several studies within the IoT accident detection domain emphasize combining machine learning with multiple sensor inputs. For instance, vibration sensors, accelerometers, alcohol detectors, and GPS/GSM modules are often integrated to reduce false positives and provide accurate accident location data. Such hybrid systems demonstrate improved accident response times but require optimization to handle environmental variability and reduce system complexity 3.OBJECTIVE: • To design and implement an automobile black box system that records critical vehicle data. • To monitor parameters such as speed, acceleration, and GPS location. • To analyze accident scenarios using recorded data for accurate reconstruction. • To assist legal and insurance investigations with tamper-proof evidence. • To improve road safety by identifying common causes of accidents and suggesting preventive measures. 4. METHODOLOGIES: 4.1 System Architecture The proposed automobile black-box system is centered on a Raspberry Pi 4 single-board computer that manages sensing, data processing, storage, and communication. The hardware integrates the following components: International Journal of Research Publication and Reviews, Vol 6, Issue 9, pp 5605-5609 September, 2025 5607 Fig.1 Automobile Black Box System • Accident detection: An MPU6050 accelerometer/gyroscope continuously monitors vehicle motion and detects sudden changes in acceleration or orientation. • Video and audio evidence: Two Raspberry Pi camera modules record continuous road and cabin video, while a microphone module captures sound inside the vehicle. • Location tracking: A NEO-6M GPS receiver provides real-time speed, position, and route data. • Emergency communication: A GSM module (SIM800L/SIM900) sends text alerts with live GPS coordinates to predefined contacts. • Driver condition monitoring: An MQ-3 alcohol sensor checks the driver’s breath for alcohol levels above a set threshold. • Door security: Magnetic door sensors log every opening and closing, supporting post-accident analysis or detection of unauthorized access. • Fire safety: A fire-detection panel adds an extra layer of hazard monitoring after a crash. • Data storage and power: High-endurance microSD/SSD cards store all data in a circular buffer. A buck converter steps the vehicle’s 12 V supply down to 5 V for the Raspberry Pi, and an optional UPS HAT provides backup power and safe shutdown to prevent data loss. 4.2 Working Procedure All sensors feed continuous, time-stamped data to the Raspberry Pi, which writes video, audio, and telemetry to the storage device in a circular buffer so older data is overwritten during normal driving. If the MPU6050 senses acceleration or tilt beyond the preset threshold, the system locks the relevant data segment and records additional information before and after the event. The GSM module then automatically sends an emergency alert containing the live GPS location to predefined contacts. Simultaneously, the cameras and microphone continue to capture evidence until the incident is fully documented. If the alcohol sensor detects a level above the safety threshold, a warning is stored and can be transmitted to family members or authorities. Door-sensor activity is logged to track entry or exit around the time of the accident, and the fire-detection panel issues an immediate alert if flames or excessive heat are present. This integrated setup ensures that every critical parameter—vehicle dynamics, driver condition, environmental hazards, and exact location—is recorded and transmitted for rapid emergency response and thorough accident analysis. 5. DISCUSSION: The proposed Automobile Black Box System for Accident Analysation is designed as a comprehensive safety and evidence-gathering platform that goes beyond the capabilities of the existing research prototypes reviewed earlier. International Journal of Research Publication and Reviews, Vol 6, Issue 9, pp 5605-5609 September, 2025 5608 Purposed system is built around a Raspberry Pi 4, chosen for its higher processing power and multiple camera interfaces, allowing simultaneous dualcamera recording of both the roadway and the vehicle interior. It integrates a broad sensor suite—including an MPU6050 accelerometer/gyroscope, MQ3 alcohol sensor, door sensors, microphone, GPS, GSM module, and a fire-detection panel—all supported by high-endurance storage and an optional UPS for uninterrupted operation. While many existing studies have made valuable contributions to accident detection and prevention, most of them are either limited in scope or rely heavily on single functionalities such as crash detection, alcohol monitoring, or GPS-based alerting. Some systems only focus on recording data for later analysis, while others emphasize immediate accident reporting but lack comprehensive evidence collection. In contrast, the proposed system stands out because it integrates multiple safety features into a single, compact unit powered by the Raspberry Pi 4. Unlike systems that are either intrusive or prone to privacy concerns, this design balances functionality and driver acceptance by using modular sensors that monitor alcohol levels, door status, and fire hazards without unnecessary invasiveness. The dual-camera setup—covering both the road and the cabin—adds a critical dimension of visual evidence that most previous works overlook, offering a reliable record for accident reconstruction and accountability. Furthermore, the use of GSM and GPS ensures not just detection, but also real-time communication of incident details to emergency contacts, significantly reducing response delays. Another advantage is the system’s ability to provide contextual data through audio recording, which complements sensor readings by capturing warnings, collisions, or abnormal sounds that could clarify accident causes. By combining proactive prevention (alcohol detection), reactive measures (automatic alerts), and investigative support (black box data storage), the proposed solution offers a holistic approach that bridges the gap left by earlier designs. This comprehensive coverage makes it more practical, reliable, and impactful for real-world adoption, ultimately improving road safety and reducing fatalities more effectively than existing solutions. 6. CONCLUSIONS: The proposed Automobile Black Box System for Accident Analysation integrates a Raspberry Pi 4 with a wide range of sensors—dual cameras, MPU6050 accelerometer/gyroscope, GPS, GSM module, MQ-3 alcohol sensor, door sensors, microphone, and a fire-detection panel—to capture every critical detail of a crash and instantly alert emergency contacts. This unified design offers stronger evidence collection, better driver-state monitoring, and enhanced safety features than the four earlier research projects reviewed, while the use of high-endurance storage and backup power ensures that data remain secure even after a severe impact. The system therefore provides a cost-effective, reliable solution that not only aids accident investigation but also helps prevent incidents through alcohol detection and fire monitoring. Future Scope Although the current prototype already surpasses previous designs, several enhancements can further improve performance and usability: • Cloud and IoT integration – automatic upload of encrypted event data to a secure cloud server for real-time access by emergency services and insurance companies. • Artificial-intelligence analytics – machine-learning models to classify accident severity, detect nearmiss events, and reduce false alarms. • Vehicle-to-Everything (V2X) communication – direct alerts to nearby vehicles and roadside units to warn other drivers and speed up rescue operations. • Mobile and web dashboards – user-friendly interfaces for drivers, families, and investigators to view synchronized video, audio, and sensor data. • Miniaturization and ruggedization – smaller, more shock-resistant enclosures and automotivegrade components for easier installation and greater durability. • Regulatory compliance and privacy features – built-in data-ownership controls, encryption upgrades, and compliance with emerging transportation safety standards. By pursuing these improvements, the system can evolve into a fully connected, intelligent accident-analysis platform that not only records crashes with high fidelity but also contributes to proactive road-safety management. 7. REFERENCES: 1. Tallapaneni, N., Gudapati, K. H., & Venkatesh, K. (2021). Automobile black box system for accident and crime analysis. International Journal of Advanced Research in Computer and Communication Engineering, 10(6), 45–49. 2. Navas, A., Shams, M. V., Ravi, N., Mirza, S. A., & Safiya, K. M. (2024). IoT-based accident detection systems – A review. International Journal of Innovative Technology and Exploring Engineering, 13(2), 112– 118. International Journal of Research Publication and Reviews, Vol 6, Issue 9, pp 5605-5609 September, 2025 5609 3. Patibandla, R. N. S., & Greer, R. (2025). Evaluating driver perceptions of integrated safety monitoring systems for alcohol impairment and distraction. Transportation Safety Journal, 7(1), 15–23. 4. 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