Accident Scenarios Research Articles

A matched case-control analysis of autonomous vs human-driven vehicle accidents

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles’ levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous’ versus Human-Driven Vehicles’ accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Use of the OSCAR-Fusion V1.4.a code for a preliminary assessment of the ACP contamination within the main ITER water cooling circuit

One of the main objectives of ITER is to produce 500 MW of power from a deuterium-tritium plasma for several seconds. This goal presents two inherent challenges: firstly, in-vessel components will require active cooling to remove the heat coming from the fusion reaction (i.e., mainly fast neutrons and alpha particles). Secondly, the materials exposed to the neutron flux will yield activated corrosion products (ACPs) in all primary cooling circuits of ITER. From a safety point of view, ACPs are one of the contributors to the Occupational Radiation Exposure (ORE), they represent a source of radiological waste and also contribute to the source term for accidental scenarios involving the loss of primary confinement. Therefore, ACPs assessment is key to estimate radiological impact for nuclear workers and the public. ITER nuclear safety engineers adopted OSCAR-Fusion v1.4.a code to assess the ACPs inventory in the Integrated Blanket ELMs and Divertor (IBED) cooling loop. This paper describes the selection of input data, the modelling of the circuits and the operational scenarios used in OSCAR-Fusion calculations. This study also examines the outcomes of such calculations, notably in terms of ACPs inventory, emphasizing the impact on the ORE and highlighting its driving parameters. Additionally, the paper offers recommendations for better ACPs management in the context of the ITER project and in accordance with the ALARA principle.

Evaluation of Fukushima Daiichi Unit 1 Ex-Vessel Phenomenon Leveraging on Primary Containment Vessel Robotic Inspections

The Great East Japan Earthquake and subsequent tsunami that struck the Fukushima Daiichi Nuclear Power Station gave ground to a set of events that resulted in an unprecedented severe accident occurring simultaneously in multiple reactors of one nuclear power station and significant release of radioactive materials into the environment. Following these events, Tokyo Electric Power Company in cooperation with domestic and international partners has made continuous efforts to clarify in detail the progression of the accident utilizing both analytical tools and robotic inspections of the damaged units. Recently, multiple inspections utilizing submersible remotely operated vehicles were conducted between December 2022 and March 2023, including the first entry into the pedestal area below the damaged reactor pressure vessel of Unit 1. This paper discusses the findings of these inspections in light of the evolving understanding of the accident scenario, leveraging insights gained through experimental and analytical studies on ex-vessel severe accident phenomena. The outcomes of these investigations have yielded new knowledge and highlighted existing gaps in our understanding. The identified knowledge gaps provide direction for future studies, ultimately advancing the level of nuclear safety.

Legal Safeguards for Tourists as Consumers: An Analysis of Accident Incidences at Tourist Sites

Accidents at tourist sites significantly harm tourists who are also consumers. This study aims to investigate both preventive and repressive forms of legal protection for tourists, especially in cases of accidents at tourist destinations. The research employs a qualitative methodology with a normative research design, focusing on statutory regulations and legal concepts. The findings indicate that preventive legal protection is lacking due to inadequate implementation of consumer protection and tourism laws, resulting in incidents with casualties, such as the one at The Geong Limpakuwus Pine Forest Tourism in Sumbang, Banyumas. Repressive legal protection is also insufficient, as the accountability of tourist sites in providing insurance to victims has not been fulfilled. Consequently, the legal protection for tourists as consumers in accident scenarios is not effectively provided.

Car Accident Detection and Alert System Leveraging Deep Learning

This paper presents a novel approach to address the critical issue of car accidents through an advanced Accident Detection and Alerting System (ADAS) empowered by cutting-edge technologies in deep learning, computer vision, and communication APIs. With road safety being a paramount concern worldwide, timely detection of car accidents followed by swift alerting mechanisms can significantly reduce the severity of accidents and save lives. The proposed system architecture incorporates the Ultralytics framework, Convolutional Neural Networks (CNN), You Only Look Once version 8 (YOLOv8), OpenCV, and Twilio API. Initially, YOLOv8 is employed to perform real-time detection of vehicles and potential accident scenarios from live video feeds obtained from roadside cameras or vehicle-mounted cameras. Subsequently, CNN models are utilized for comprehensive analysis of detected objects to discern potential accidents based on predetermined criteria, including sudden deceleration, collision patterns, and anomalous vehicle movements. Upon successful detection of an accident, the system triggers an alert mechanism via the Twilio API, which delivers instantaneous notifications to emergency services, nearby medical facilities, and designated contacts of the involved parties. These alerts are enriched with crucial details such as the accident location, vehicle types involved, and severity assessments derived from CNN analyses. Furthermore, the integration of OpenCV facilitates preprocessing tasks such as noise reduction, image enhancement, and object tracking, thereby enhancing the system's robustness and accuracy in diverse environmental conditions and lighting scenarios. The proposed ADAS offers numerous benefits, including real-time accident detection, automated alerting of emergency services, and minimized response times, ultimately contributing to mitigating the severity of accidents and preserving lives. By harnessing the power of deep learning algorithms, advanced computer vision techniques, and seamless communication APIs, this system represents a significant step towards fostering safer road environments within smart cities and transportation networks.

Thermal Tests of Type B Radioactive Transport Package: Experimental Description and Heat Flux Analysis

ABSTRACTThe design of Type B radioactive transport package should be able to withstand the fire accident scenario in transportation accident conditions. For the simulation of the fire scenario, there are mainly three methods: pool fire test, furnace test and thermal radiation test. This paper tried to carry out the fire test with the furnace method that the conditions in the furnace can be more carefully controlled. The paper described the requirements of fire test, test conditions and measurement test methods and analysed the temperature change of fire site and the heat flux data of the surface of the package during the fire test. The test results seemed that the fire test method could provide satisfactory thermal environment for fire test of Type B radioactive transport package.

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

SIMMER code two-fluid model well-posedness and stability for simulating liquid metal (lead–lithium eutectic), water vapor, and non-condensable gases multi-component multi-phase flows

Liquid metals are employed as a coolant in liquid metal fast reactors (LMFR) and are considered breeders and coolants for future power-producing fusion reactors. The interaction of liquid metals with other coolants, such as air and water, is one of the possible occurrences in liquid metal-cooled reactors. Although the SIMMER-III computer code is currently in the validation and verification phase, it is a prospective candidate code for correctly investigating possible accidents. This study aims to determine the stability and well-posedness of the SIMMER-III code Eulerian-Eulerian two-fluid model (TFM) under any such accident scenario. This work also considers the influence of the virtual mass force and diffusion forces in momentum on TFM stability in accelerated liquid metal, steam, and non-condensable gaseous flows throughout all flow regimes. The characteristics method is used to assess the ill-posed nature of TFM for all types of accidents and accident scenarios in a hypothetical simplified system model with several components (Lead–Lithium, non-condensable gases, and water vapor). It has been discovered that the analysis findings vary from the air and water two-component two-phase flows (characteristics roots spectrum and error growth rate patterns) and are particularly sensitive to the diffusion and virtual mass coefficients. This is because liquid metals have a higher density than liquid water and steam, resulting in strong virtual mass forces and weak diffusion forces in liquid-metal and gas two-phase flows. Because of the extensive range of possible interactions between fluxes of different components, producing an accurate representation of diffusion in multi-component mixtures is difficult. Because of this, it is strongly suggested that the virtual mass coefficient and diffusion coefficients be handled more accurately for these kinds of flows. The values of these coefficients significantly affect how accurate proposed TFM predictions are, but there hasn't been much research on how to estimate them. The study also sheds light on the model's accuracy and highlights the areas where the model's predictions will be mathematically trustworthy.

Accident Prediction and Emergency Response System Using Accelerometer Sensor

Accidents, being unforeseeable events, can result in serious consequences, particularly when immediate medical attention is necessary. To address this issue, we propose the development of an Accident Prediction and Emergency Response System (APERS) that utilizes mobile accelerometer sensors. The APERS comprises two key components: a mobile application installed on users' smartphones and a web-based application for hospitals. The mobile application enables users to input and update their personal information and medical records, which are securely encrypted and stored. Moreover, the application continuously monitors accelerometer sensor data in real-time. Upon detecting a sudden spike in accelerometer readings indicative of an accident, the user's encrypted data and medical records are automatically transmitted to the hospital-side web application. On the hospital side, the web-based application receives and decrypts the user data, granting medical professionals access to vital information even if the user is unconscious. This facilitates timely and well-informed medical interventions, potentially saving lives. The system operates seamlessly, providing a robust solution for accident prediction and emergency response. The APERS offers numerous benefits, including proactive accident prediction, automated data sharing, and swift medical response. By leveraging mobile technology and accelerometer sensors, it addresses the pressing need for efficient emergency services in accident scenarios. Looking ahead, users could potentially share data while crossing the reception, further enhancing the system's effectiveness.

A new perspective on density and strength loss profiles at the surface of thermally oxidized nuclear graphite

Oxidation of graphite components could influence their designed life in a high-temperature nuclear reactor. The oxidized regions could potentially lower the allowed stress capacity. The American Society of Mechanical Engineers rules for the design and construction of graphite-moderated reactors recommend that subsurface regions that might become excessively damaged by oxidation during reactor operation be identified and excluded from geometry and stress calculations. Identification of oxidation-affected regions is possible, in principle, through complex modeling exercises of reactor behavior during hypothetical accident scenarios coupled with graphite oxidation models, but this procedure may not have the precision needed for informed decisions. This paper proposes an alternate method, based on interpretation of a series of well-designed oxidation experiments, which could augment the designer's tools. The procedure is illustrated by data on oxidation by air of several graphite grades (NBG-18, PCEA, IG-110, R4-650) that are corroborated with independent literature information, when available. The Wichner model for graphite oxidation used for this analysis provides conservative results that could be quickly implemented in the design process.

Early-phase core degradation interpretation by interaction between fuel cladding and fuel components in Fukushima Daiichi nuclear power plant Unit 2

ABSTRACT The early phase of the core degradation at the Fukushima Daiichi Nuclear Power Station Unit 2 was investigated in terms of a fuel-component degradation by materials interaction by carrying out the accident analysis, evaluation of chemical conditions and experimental works. First, the accident scenario was evaluated to specify a target period (from 18:40 to 22:40 on NaN Invalid Date NaN), and then the temperature and H2/H2O pressure ratio in the core region during this period were calculated based on plant parameters. Subsequently, semi-integral experiments were performed using the mocked-up fuel components of a boiling water reactor (BWR). Furthermore, separate-effects tests using Ni-alloy/Zircaloy4 were also performed to understand the phenomenon observed in the semi-integral experiments, where the Ni-Zr reaction occurred even with the oxide layer on the Zr-based alloy. Consequently, it was estimated that the Zr-Ni eutectic melting reaction above 1100°C under steam-starved conditions resulted in a fuel failure at 19:08, melting of spacer locations at 19:37, and upper-tie-plate at 21:26. The first and second degradations could have caused the release of radioactive elements or particles leading to the neutron detection at 21:00 in front of the main gate, and the third degradation could have resulted in the separation of the upper-tie-plate.

Application of reinforcement learning to deduce nuclear power plant severe accident scenario

Severe accident scenarios for nuclear power plants are determined through probabilistic safety analysis (PSA). In this process, it is possible to identify the failure sequence of specific components, but assessing the impact of component failure time on the severity of accident remains a challenge. In this study, a novel approach is presented that utilizes machine learning methodologies such as reinforcement learning (RL) to complement traditional PSA. The proposed process was validated by comparing whether the most severe accident scenarios obtained through critical accident simulations can be reproduced by a RL, since this is a novel use of machine learning techniques. The comparison shows feasibility of exploring critical accident scenarios using RL. To implement the reinforcement learning methodology based on the existing system code, supervised learning model that can predict the remaining time of reactor vessel failure was implemented in this study. Based on this prediction model and data from existing catastrophic accident simulation, RL was implemented. The results obtained from RL were subsequently validated with the results of severe accident code simulation. In summary, new methodology for applying machine learning techniques to the nuclear accident analysis process was presented, and the feasibility and potential of the proposed methodology were discussed.

Uranium-zirconium hydride nuclear fuel performance in the NaK-cooled MARVEL microreactor

The Microreactor Applications Research Validation and EvaLuation (MARVEL) Project is producing a high temperature NaK-cooled nuclear reactor test bed at the Idaho National Laboratory to ultimately improve integration of microreactors to end-user applications. This ambitious effort seeks to design, authorize, construct, test, and operate the reactor within five years. The computational software package chosen for high fidelity fuel performance modeling, BISON, has been upgraded to model the performance of the MARVEL reactor's selected fuel system: 304 stainless steel-clad U-ZrHx. In this manuscript, we begin by describing the MARVEL fuel element. The known properties and relationships of the fuel element constituents are recapitulated from data available in the literature. These historical relationships assume a particular U-ZrH microstructure which previously was not well-understood; therefore, the fuel's microstructure is experimentally confirmed using electron microscopy and X-ray diffraction. This information is then used to analyze the thermomechanical performance of the MARVEL fuel element during a hypothetical extreme accident scenario (a loss of flow accident during a loss of coolant pressure accident) using the nuclear fuel performance code BISON. The analysis shows that the primary phenomenon which could compromise the structural integrity of the MARVEL fuel element is the same as for TRIGA reactors: high temperature internal gas overpressurization-induced rupture of the cladding arising from excessive hoop stresses. Despite modeling the most severe MARVEL accident scenario, the highest anticipated temperature in the fuel, about 718 °C, results in hoop stresses of less than 10 MPa. Based on this analysis, a conservative steady state fuel meat temperature limit of 900 °C precludes cladding damage. The newly incorporated fuel performance simulation capabilities developed in this work may also be extended to model this fuel system under other conditions or reactors.

Алгоритм оценки последствий отказа технологического трубопровода по методике API RP 581 3rd Edition (Уровень 1)

The article considers the issue of the development of an algorithm for the assessment of the consequences of failure (CoF) of the process pipeline of the gas condensate stabilization system of a hazardous production facility. The assessment of the consequences of failure is one of the main components of a risk-oriented approach to the inspection of technical devices. In accordance with the API RP 581 3rd Edition methodology, the assessment is used for equipment ranking and designed to determine the priority of inspections. Inspection, in turn, is a tool to ensure integrity, i.e., the condition of equipment that ensures its leak-tightness, which undoubtedly is a necessary condition of industrial safety. The algorithm and the detailed procedure of calculation of failure consequences consisting of 13 basic stages have been provided. As an example, the calculation of failure consequences has been conducted by the API RP 581 3rd Edition methodology (Level 1). The basic stages and the results of calculation have been compared to the calculation methodologies of technical device failure consequences effective in Russia. The results of the comparative analysis of methodologies indicate their similarity as to the application of common methods of the analysis of accident scenarios, the use of similar initial data, and the focus on the reduction of equipment failure risk. At the same time, significant differences in indices and criteria of acceptable risk level have been detected. The results of the calculation indicated the conservatism of the API 581 3rd Edition methodology (Level 1). The application of calculation of failure consequences by the API RP 581 3rd Edition methodology (Level 1) in the field of risk-oriented approach to the inspection of technical devices is possible upon its adaptation to the standards generally accepted in Russia.

A novel unmanned aerial vehicle driven real-time situation awareness for fire accidents in chemical tank farms

A large number of flammable hazardous materials are stored in chemical tank farms, where fire-induced domino accidents can be easily triggered. In this study, a novel real-time fire situation awareness (FSA) approach based on UAV is proposed to capture spatio-temporal evolution characteristics and predict development trends of fire accidents. Firstly, fire images are acquired by UAV, and the key parameters of fire are extracted in real time based on YOLOv8 network. Then, the thermal radiation and impact on surrounding equipment are predicted by combining LSTM network, solid flame model and improved probit model. The proposed method is verified by small-scale tank fire experiments, which demonstrate its superiority in terms of physical consistency and prediction accuracy. The results show that the mean absolute percentage error (MAPE) of fire parameter extraction is not higher than 5.43%, the MAPE of thermal radiation prediction is not higher than 25%, and the dynamic time to failure (dttf) for the model tank at different location is predicted. This work has the potential to provide a novel solution for real-time assessment of fire size and trend prediction to support firefighting, emergency rescue and decision making in fire accident scenarios.

An injury risk-based comprehensive framework for testing and assessing ADAS functions in critical road scenarios

Assisted driving is currently considered a key aspect for improving road safety, and automakers and OEMs are working to achieve higher levels of vehicle automation by introducing new technologies and Advanced Driver Assistance Systems (ADAS) in the circulating fleet. This trend requires test protocols for vehicle safety assessment to be frequently reviewed and updated, considering the latest advances in the state of the art regarding ADAS functions and systems. As of today, performance assessment programs (such as NCAP) mainly evaluate how an ADAS behaves in terms of crash avoidance in specific critical scenarios, which represent the most frequent crash constellations among real-world impacts. However, enhanced safety can be also obtained in case the impact is not avoided if a decrease in Injury Risk (IR) for the involved road users is achieved by ADAS intervention, compared to the case of no intervention.The purpose of this work is to propose an overall framework to draft or update test protocols for ADAS performance assessment based on real car-to-car impact observations, representative of impact scenarios in terms of both occurrence frequency and IR. First, the in-depth accident database IGLAD is analyzed to identify the most relevant car-to-car accident scenarios based on a relevance indicator, i.e., the risk level being the multiplication of the occurrence frequency and IR for a specific scenario. For each relevant scenario, a risk level-based strategy to identify one significant closing speed between vehicles for the tests is defined; the test collision speed for the two vehicles is determined analogously, and the risk level for each combination of speeds in a scenario represents the maximum achievable score by the ADAS if the collision is averted. Considering the well-established Euro NCAP framework as a relevant starting point for the definition of test protocols, two examples are highlighted regarding the proposal of a new test protocol and an update of an already existing one. Finally, a method is proposed for ADAS performance assessment if the impact is not avoided, scaling the maximum achievable score based on the IR reduction consequent to the ADAS intervention.

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.

Experimental investigation on the behavior of thermal super insulation materials for cryogenic storage tanks in fire incidents

The number of vehicles using or transporting cryogenic fuels such as Liquefied Hydrogen (LH2) or Liquefied Natural Gas (LNG) increases fast in the land transportation sector. Does this also entail new risks? The storage of cryogenic fuels requires tanks with Thermal Super Insulations (TSI) to keep the fluid cold and limit the formation of boil-off gas. TSI has proven itself in some applications since the middle of the 20th century, but in the land transport sector they are still quite new, where accidents involving fires, collisions, and their combination are to be expected. This work focuses on investigating the behavior of different types of TSI while exposed to a heat source representing a fire. To this aim, a High-Temperature Thermal Vacuum Chamber (HTTVC) was applied, which allows the thermal loading of a thermal insulation material in a vacuum and measuring the heat flow transported through the TSI in parallel. In this study, the results of 6 samples are presented regarding 3 types of MLI, rock wool, perlites, and microspheres. The thermal exposure caused different effects on the samples. In practice, this can be connected to the rapid release of flammable gases as well as to a Boiling Liquid Expanding Vapour Explosion (BLEVE). These results are relevant for reducing the risks to people and infrastructures in the progressive establishment of tanks for cryogenic fluids in our industry and society. The data presented in the study can be used to improve the design of tanks and TSIs, the assessment of accident scenarios, and the development of measures for first responders.

Interaction of CsOH with structural steel 316L and simulated products of the steel-cladding reaction under assumed conditions relevant for the accident at the Fukushima Dai-Ichi Unit 2 and 3

A high radiation dose rate under the shield plugs of the Units 2 and 3 reactors at the Fukushima Dai-Ichi Nuclear Power Station (1F), raised an important question on volatile radioactive Cs migration, adsorption and retention on various structural materials during the accident. The high dose rate interferes with decommissioning work. The purpose of this study is to present basic knowledge on contamination mechanisms of a BWR reactor structural materials and post-accident debris by Cs compounds to help decommissioning companies consider the ways of decontamination and debris handling during 1F debris retrieval. Having this goal, a series of tests on CsOH contamination mechanism under assumed accident conditions for Units 2 and 3 was performed. The results may improve the accuracy of estimating the situation inside the reactor from the perspectives of accident analysis and materials’ properties. The test temperature and compounds were chosen based on the probable accident scenario. In particular, Cs adsorption was checked using preoxidized 316L stainless steel, a product of control blade degradation (steel-B4C eutectic) and a product of steel-channel box and cladding interaction (Zr-Fe based multiphase material). Penetration ability of Cs into the oxidized and metallic layers of materials was evaluated. CsOH most probable adsorption temperature range and new Cs containing phases’ formation temperature range were followed.

ACCIDENT DETECTION USING TENSOR FLOW AND CNN

Accident detection is a critical aspect of road safety management, with timely identification and response being paramount to minimizing casualties and damage. In this project, a comprehensive approach to accident detection utilizing Deep learning techniques is proposed. The system aims to automatically detect accidents in real time using information obtained from diverse sources like video feeds, sensor inputs, and audio signals. The project involves the development and implementation of Deep learning models trained on labeled datasets containing examples of accident and non-accident scenarios. Supervised learning techniques like classification, regression and advanced neural network architecture like convolution, recurrent neural network are considered to effectively process and analyze the data. The deployed system continuously monitors the environment, processing incoming data streams to identify patterns indicative of accidents. Upon detection, the system triggers appropriate response mechanisms, such as alerting emergency services or nearby vehicles, facilitating swift intervention. The project aims to contribute to road safety initiatives by providing a scalable and efficient solution for accident detection. Additionally, the insights gained from the data collected by the system can inform policymakers and urban planners in implementing measures to prevent accidents and improve overall road safety. Through this project, the students gain valuable experience in applying Deep learning techniques to address real-world challenges and contribute to societal welfare.