Relevant bibliographies by topics / Accident detector

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Accident detector'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Accident detector"

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Dorji, Kinley, Norbu Wangdi, Sonam Pelden, Tshering Tenzin, and Dechen Lhamo. "Vehicle Accident Detector and Notifier." Zorig Melong | A Technical Journal of Science, Engineering and Technology 5, no. 1 (2021): 80–85. https://doi.org/10.17102/zmv5.i1.015.

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This paper presents a system to detect traffic accidents, notify emergency responders and store pictorial data for postaccident analysis. The proposed system is basically divided into three phases; the accident detection phase, the notification phase and the storage phase. The accident detection phase has core components as MEMS and vibration sensor. The unusual vibrations sensed and the tiltation or toppling of the vehicle are understood by vibration sensor and MEMS respectively as an occurrence of accident. The main components of the accident notification phase are GSM and GPS whereby the GPS tracks the coordinates (latitude and longitude) at which the vehicular accident has happened and the time during which the accident occurred. The GSM sends these information to emergency responders and concerned authorities through SMS. The storage phase consists of OV7670 camera and SD card where the camera captures the pictures during the accident and save the image data in SD card. Through interfacing of these various sensors with Arduino programming, the prototype was able to detect an impact and send coordinates through GSM using SMS. Similarly, the image data captured during the impact was also able to store in the SD card. The proposed system has an advantage of getting people’s lives saved and reduce statistics of death rate due to vehicle accident by providing faster response to the vehicle accidents.
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Mohan, M. Khambalkar, H. Panchal Parul, and Joshi Kashyap. "Car Accident Detector and Informer System." Journal of Advancement in Electronics Design 8, no. 1 (2024): 8–17. https://doi.org/10.5281/zenodo.14435694.

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<em>Road safety is a critical concern in the modern world, where advancements in technology have made our lives easier but have also given rise to increased traffic hazards and road accidents. The "Car Accident Detector and Informer" project is an innovative system aimed at enhancing road safety by accurately detecting and informing about car accidents in real-time. This project integrates advanced sensors, microcontroller technology, and communication protocols to create an efficient accident detection and notification system. Utilizing GPS and GSM technology for precise location tracking and instant notifications, this system has the potential to reduce emergency response times, save lives, and minimize property damage. This research paper presents a detailed overview of the project, including its objectives, working principles, components, advantages, disadvantages, and future prospects.</em> <strong><em>&nbsp;</em></strong>
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ChandrashekharSinfal, SairamPatil, SiddharthHavale, Pujari Shivanand, and Mr.A.M.Pandhare. "Automobile Accident Detector using Wireless Communication." Journal of Automation and Automobile Engineering 5, no. 1 (2020): 11–12. https://doi.org/10.5281/zenodo.3750868.

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With the advent of science and technology in every walk of life the significance of vehicle safety has increased and the main priority is been given to decrease in accident on roads. This causing every death on road accidents every 20 seconds in the world according to WHO. The Microcontroller alone with accelerometer sensor, GPS and GSM modules shorten the alarm time to a large amount and locate the site of accident accurately. Subsequently the time for searching the location is reduced and the person can be treated as soon as possible which will save many lives. In the existing accident detection systems, there is a problem of false alarms or situations where immediate help is not necessary. In such cases the driver must be able to manually switch off the alert system and stop the sending of message.
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Daw, Khaing Zar Win. "Design and Implementation of Alcohol Detector and Accident Detection System using GSM Modem." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1086–90. https://doi.org/10.5281/zenodo.3590532.

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Nowadays, the number of vehicles is increasing significantly every year. Many accidents are occurring because of the alcohol consumption of the driver. After drinking alcohol, driving is the most reason for accidents in almost all countries all over the world. Therefore, alcohol detection system and automatic finding car accident place using GSM modem and GPS are presented in this paper. The combination purpose of alcohol detection and car accident detection systems is to save human life. If the car driver drinks alcohol, the alcohol sensor will sense alcohol concentration, will display it on the LCD and will turn off the car engine. In the accident detection system, the vibration sensor senses vibration when an accident occurs. If the sensor senses the car accident, the vibration sensor will give the digital output to the microcontroller. By using the GPS module, the latitude and longitude of the place where the accident happened are sent by message to the assigned phone number through the SIM900 GSM Modem. By implementation of this system, human beings can be protected from harm and other non desirable outcomes on road accidents. Daw Khaing Zar Win &quot;Design and Implementation of Alcohol Detector and Accident Detection System using GSM Modem&quot; Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26595.pdf
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Kim, Il-Gyu, Ik-Sang Lee, and So-Young Kim. "Accuracy and Precision Evaluation of Unmanned Automatic Gas Detection Tube Measurement Device Using IoT." Fire Science and Engineering 38, no. 3 (2024): 57–64. http://dx.doi.org/10.7731/kifse.0342fdae.

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The number of chemical accidents occurring nationwide when handling chemicals increased from 66 in 2022 to 116 in 2023. When a chemical accident occurs, response personnel must quickly detect the substances causing the accident and establish a plan for disaster prevention and resolution. However, due to the hazards and risks associated with accidents, ensuring the safety of response personnel while determining the origin of the accident is challenging. Generally, easy-to-use manual detector tubes are widely used during initial accident responses; however, using personal protective equipment, such as gloves and protective clothing, makes determining the origin difficult. An unmanned gas detection tube measuring device was patented in a previous study to address this issue. However, its use is limited because the accuracy of the device has not been evaluated. Therefore, in this study, we evaluated the accuracy and precision of the unmanned gas detector. Ammonia was selected as the target substance because it frequently causes accidents. To ensure the reliability of the unmanned gas-measuring device, a comparative analysis was conducted using the manual detector with a single gas-measuring device as a sensor. The manual detector showed a higher error compared to the standard error due to the delay in reading time and deviation in the individual reading. However, the measurement error of the unmanned gas detector was 19.8%, which was within the manual detector measurement error range; the accuracy was higher than that of the manual detector. Therefore, an unmanned gas detector is preferable over a manual detector.
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Aravind., V.H, S. Harikrishnan., Krishna Raj. P. M. Hari, and Krishnan S. Ramesh. "Automatic Alcohol Detector." Recent Trends in Automation and Automobile Engineering 4, no. 1 (2021): 1–8. https://doi.org/10.5281/zenodo.4778037.

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<em>The purpose of this project is to prevent the vehicle accidents by using an alcohol detector in an effort to reduce traffic accident cases based on driving under the influence of alcohol. This project is developed by integrating the alcohol sensor with the microcontroller. The alcohol sensor used in this project is MQ-3 which is used to detect the alcohol content in human breath. The alcohol content level in blood is detected by the alcohol sensor from the human breath. The ignition system of the vehicle is operated based on this level thus providing as a safeguard against drunken driving.</em>
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Bhawarthi, Amruta A., Abhishek Katore, Soham Kature, et al. "Alcohol Detector with Alert Notification." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 520–27. http://dx.doi.org/10.22214/ijraset.2023.56027.

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Abstract: Drunk driving is believed to be one of the leading causes of road accidents, killing over 1.5 million people annually. A driver who is driving under the influence of alcohol is not only putting his own life at risk but also others too. The aim of our research paper is to avoid these accident rates which have caused due to drinking and driving. This research report suggests a novel and pioneering technique to lessen accidents brought on by drunk driving. With the aid of an Alcohol detection sensor (MQ3) sensor, the system continuously checks the alcohol concentration level, and if it rises above a certain level, it notifies the police via the GSM SIM800L module.
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Patekar, Kedar. "AVOIDING ACCIDENTS WITH THE HELP OF SMOKE DETECTOR." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02778.

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Smoke detectors are responsible for preventing accidents by sensing harmful levels of smoke before they can cause serious health hazards. The devices prevent accidents by avoiding toxic air exposure, making them a safety measure in homes, offices, and industries. This review discusses the methodologies, implementation strategy, and performance of smoke detectors in accident prevention. The functional mechanisms of components, system function, and future developments are presented, highlighting their significance in contemporary safety infrastructure.
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Archana, Myaka. "Automatic Vehicle Accident Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2975–79. http://dx.doi.org/10.22214/ijraset.2021.35670.

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The ascent of technology and infrastructure has created our lives easier. the appearance of technology has conjointly enhanced the traffic hazards and therefore the road accidents ensue ofttimes that causes immense loss of life and property owing to the poor emergency facilities. Our project can give AN optimum resolution to the present flinch. AN measuring device may be utilized in a automobile alarm application in order that dangerous driving may be detected. It may be used as a crash or change detector of the vehicle throughout and when crash. With signals from AN measuring device, a severe accident may be recognized. in step with this project once a vehicle meets with AN accident straight off vibration detector can notice the signal or if a automobile rolls over, and small electro system (MEMS) detector can notice the signal and sends it to ARM controller. Microcontroller sends the alert message through the GSM electronic equipment together with the situation to the police room or a rescue team. therefore the police will straight off trace the situation through GPS electronic equipment, when receiving the data. Then when orthodox the situation necessary action are going to be taken. If the person meets with a little accident or if there's no serious threat to any ones life, then the alert message may be terminated by the motive force by a switch provided so as to avoid wasting the dear time of the medical rescue team. This paper is helpful in police work the accident exactly by suggests that of each vibration detector and small electro system (MEMS) or measuring device. As there's a scope for future implementation we will add a wireless net cam for capturing the photographs which can facilitate in providing drivers help.
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Totsuka, Yoji. "Accident of the Super-Kamiokande detector." TRENDS IN THE SCIENCES 7, no. 5 (2002): 63. http://dx.doi.org/10.5363/tits.7.5_63.

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Dissertations / Theses on the topic "Accident detector"

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Petito, Anthony Bruno 1967. "Design of a shield system for a hyper-pure germanium detector as a stack monitor for use in accident conditions at a nuclear power plant." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/278343.

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Collimator and shield configurations for two high-purity germanium detectors were designed for use during a loss of coolant accident at a boiling water reactor. The detectors will return information concerning stack releases to operators within a 15 minute time frame. Operating parameters for the stack monitors are defined by the United States Nuclear Regulatory Commission (USNRC) and a 24 hour source term generated by ORIGEN2. A lead collimator 0.4 cm in diameter, 20 cm in length for the high range detector and 2 cm in diameter, 20 cm in length for the low range detector was shown through a Monte Carlo code, MCNP4 to prevent high range detector saturation and provide enough low range detector response so good statistical data on stack releases result. A lead shield 20 cm thick was shown through MCNP4 to reduce the background radiation interference for both detectors to levels such that the detection of isotopes within the stack effluent is possible as required by the USNRC.
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Gomes, Vítor Emanuel Ornelas. "Smartphone based accident detection and eCall implementation." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/12835.

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Mestrado em Engenharia Electrónica e Telecomunicações<br>Intelligent Transportation Systems are emerging, to increase safety, e - ciency and comfort on roads. This intelligence is due to the fact that new technologies are being introduced in the most recent automobiles. As a result of this technological evolution, vehicular communication systems are being developed, to provide drivers with more information about the interventionists present in the roads they circulate. Predictions point that this information can increase safety and e ciency on roads. Presently, the Instituto de Telecomunica c~oes de Aveiro, is developing its own vehicular communication system, named HEADWAY, as a solution. HEADWAY DSRC 5.9 GHz vehicular communication system currently under development. Smartphones nowadays are very popular devices. This is due to the fact that they pack incredible hardware resources in a small and portable device and the possibility to third party developers, develop applications for them. This enables these devices to be used in di erent areas, depending only from the creativity of the developers. To diminish the number of fatalities due to road accidents, the European Commission has mandated the implementation of eCall in every new vehicle by 2015. In vehicles, the eCall aim to detect accidents and request accidents automatically. This dissertation targets, on the one hand, the development of an accident detection mechanism with eCall implementation. On the other hand it targets the integration of smartphones with HEADWAY, by developing an application that takes advantage of the system characteristics and demonstrates it. To achieve the proposed goals, an Android application was developed which acts as an HMI for HEADWAY, enables message exchange between vehicles, automatically detects accidents and proceeds with a help request. Most of the proposed goals where achieved, except the eCall implementation, which an alternative method was developed.<br>Os Sistemas de Transporte Inteligentes estão a emergir, de forma a introduzir mais segurança, eficiência e conforto nas estradas. Esta inteligência deve-se ao facto de novas tecnologias estarem a ser introduzidas nos automóveis recentes. Como resultado da evolução tecnológica os sistemas de comunicação veiculares estão a ser desenvolvidos, com o objectivo de munir os condutores com informações relativas aos diferentes intervenientes da estrada onde circulam. Prevê-se que este tipo de informação leve a uma maior segurança e eficiência nas estradas. Actualmente no Instituto de Telecomunicações de Aveiro, está a decorrer um projecto que visa fornecer uma alternativa como sistema de comunicações veiculares. Este projecto tem o nome de HEADWAY. O HEADWAY é um sistema de comunicações veiculares DSRC 5.9 GHz, atualmente em desenvolvimento. Os smartphones hoje em dia já são dispositivos estabelecidos no mercado. Isto deve-se ao facto destes apresentarem um grande potencial, ao integrarem recursos de hardware incríveis num pequeno dispositivo e de permitirem o desenvolvimento de aplicações por terceiros. A criatividade dos programadores tem permitido a utilização destes dispositivos em diversas áreas. De forma a diminuir o número de mortes causadas por acidentes rodoviários, a Comissão Europeia, tornou obrigatório que em 2015 todos os novos carros estejam equipados com o sistema eCall, que visa a deteção de acidentes e pedido de ajuda ao 112 automáticos. Esta dissertação tem por um lado, o objectivo de desenvolver um detector de acidentes com implementação de eCall, e, por outro lado, integrar um smartphone com o HEADWAY, através do desenvolvimento de uma aplicação que tire partido das características deste sistema e assim o demonstre. Para cumprir os objectivos foi desenvolvida uma aplicação para Android que atua como HMI para o HEADWAY, facilita a troca de mensagens entre veículos, deteta automaticamente acidentes e procede com pedidos de ajuda. Na conclusão do projecto, verificou-se que os objectivos propostos foram na sua maioria concluídos, exceptuando a implementação da eCall ao 112, sendo desenvolvido um método alternativo.
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Černoch, Adam. "Vybrané způsoby zlepšení orientace řidiče v dopravním prostoru." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-233053.

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The aim of this thesis is to find suitable measures which will lead to the improvement of driver orientation in traffic spaceleading to lower the accident rate in the area. The theoretical part describes the traffic surveys that relate to the topic. Furthermore, the work mentioned detectors used for the implementation of automated traffic surveys. The theoretical part also describes methods that can be used to analyze the selected location and then design the ideal modification. At the end gives an overview of the various measures. In the practical part are different methodologies used for the analysis of selected intersections. Firstly, the analysis of observed conflict situations, including making conflicting diagram, under which was designed to measure. Then, the analysis of traffic accidents. Again, the result was a proposal for possible actions. The main objective was to compare and appreciation of both methodologies and proces design measures to improve driver orientation in the selected location.
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Lawoyin, Samuel. "Novel technologies for the detection and mitigation of drowsy driving." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3639.

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In the human control of motor vehicles, there are situations regularly encountered wherein the vehicle operator becomes drowsy and fatigued due to the influence of long work days, long driving hours, or low amounts of sleep. Although various methods are currently proposed to detect drowsiness in the operator, they are either obtrusive, expensive, or otherwise impractical. The method of drowsy driving detection through the collection of Steering Wheel Movement (SWM) signals has become an important measure as it lends itself to accurate, effective, and cost-effective drowsiness detection. In this dissertation, novel technologies for drowsiness detection using Inertial Measurement Units (IMUs) are investigated and described. IMUs are an umbrella group of kinetic sensors (including accelerometers and gyroscopes) which transduce physical motions into data. Driving performances were recorded using IMUs as the primary sensors, and the resulting data were used by artificial intelligence algorithms, specifically Support Vector Machines (SVMs) to determine whether or not the individual was still fit to operate a motor vehicle. Results demonstrated high accuracy of the method in classifying drowsiness. It was also shown that the use of a smartphone-based approach to IMU monitoring of drowsiness will result in the initiation of feedback mechanisms upon a positive detection of drowsiness. These feedback mechanisms are intended to notify the driver of their drowsy state, and to dissuade further driving which could lead to crashes and/or fatalities. The novel methods not only demonstrated the ability to qualitatively determine a drivers drowsy state, but they were also low-cost, easy to implement, and unobtrusive to drivers. The efficacy, ease of use, and ease of access to these methods could potentially eliminate many barriers to the implementation of the technologies. Ultimately, it is hoped that these findings will help enhance traveler safety and prevent deaths and injuries to users.
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Li, Yun Popescu Mihail. "Fall detection using sound sensors." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6651.

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Title from PDF of title page (University of Missouri--Columbia, viewed on March 10, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Mihail Popescu. Includes bibliographical references.
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Balraj, Navaneethakrishnan. "AUTOMATED ACCIDENT DETECTION IN INTERSECTIONS VIA DIGITAL AUDIO SIGNAL PROCESSING." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-10212003-102715/.

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The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of ?crash? or ?non-crash?. In the multi-class system, the output is the label of ?crash? or various non-crash incidents including ?pile drive?, ?brake?, and ?normal-traffic? sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying ?crash? or ?non-crash? on a given input audio signal.
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Hiemer, Marcus. "Model based detection and reconstruction of road traffic accidents." Karlsruhe : Univ.-Verl, 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974366552.

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Hamdane, Hedi. "Improvement of pedestrian safety : response of detection systems to real accident scenarios." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4091.

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Le contexte général de cette recherche concerne la sécurité active des piétons. De nombreux systèmes embarqués dans les véhicules sont actuellement développés afin de détecter un piéton sur la chaussée et d’éviter une collision soit par une manœuvre de freinage d’urgence soit par une manœuvre de déport. La plupart de ces systèmes d’aide à la conduite sont basés sur des systèmes de détection (caméras, radars, etc). Ils analysent la scène en temps réel, puis effectuent un traitement d’images dans le but d’identifier un potentiel danger. Or il apparaît difficile de déterminer la pertinence de ces systèmes en termes de sécurité routière. L’'objectif général de ce travail est ainsi d’estimer cette pertinence en confrontant les systèmes à de multiples configurations d’accidents réels. La méthodologie consiste à tester les systèmes de détection des piétons dans les configurations d’accidents reconstruits en les associant à la cinématique des véhicules. Le test de performance de ces systèmes a été alors réalisé en vérifiant leurs compatibilités au regard de la chronologie des accidents; i.e. vérifier la possibilité d’'évitement des accidents. À partir de ces reconstructions d’accidents réels, une analyse a été réalisée afin de dégager les enjeux au niveau spatio-temporelle qui influencent la sécurité primaire du piéton<br>The scope of this research concerns pedestrian active safety. Several primary safety systems have been developed for vehicles in order to detect a pedestrian and to avoid an impact. These systems analyse the forward path of the vehicle through the processing of images from sensors. If a pedestrian is identified on the vehicle trajectory, these systems employ emergency braking and some systems may potentially employ emergency steering. Methods for assessing the effectiveness of these systems have been developed. But, it appears difficult to determine the relevance of these systems in terms of pedestrian protection. The general objective of this research was to test the response of these systems in many accident configurations.The methodology consisted of coupling the vehicle dynamic behaviour with a primary safety system in order to confront these systems to real accident configurations. The relevance of these systems is studied by verifying the feasibility of deploying an autonomous emergency manoeuvre during the timeline of the accident and according to the vehicle dynamic capabilities: i.e. verifying the possibilities in terms of crash avoidance. From these accident reconstructions and simulation, factors relevant to the primary safety of pedestrians were deduced
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Khaghani, Farnaz. "A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98801.

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Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems.<br>M.S.<br>Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.
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Zino, John Frederick. "Monte Carlo based exposure rate response estimates for criticality accident detectors at the Savannah River site." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/17554.

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Books on the topic "Accident detector"

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Lee, Child, Kinsella Sophie, Rosenfelt David, Crocker Gareth, and Reader's Digest Association, eds. Select Editions: Volume 1 2009. Reader's Digest Association, 2009.

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Chung, Edward C. S. Effective incident detection and management on freeways. ARRB Transport Research, 1999.

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J, Murphy Dennis. Using sensors on agricultural equipment to reduce human risks. U.S. Dept. of Agriculture, Agricultural Research Service, National Agricultural Library, Technology Transfer Information Center, 1996.

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Nalluri, Vihari. GPS & GSM based automated accident detection system. National University, 2009.

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Hiemer, Marcus. Model based detection and reconstruction of road traffic accidents. Universita tsverlag Karlsruhe, 2005.

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Fuhrmann, Mark. Early leak detection external to structures at nuclear power plants. United States Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, 2013.

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Office, General Accounting. Child labor: Increases in detected child labor violations throughout the United States : report to the Honorable Don J. Pease, House of Representatives. U.S. General Accounting Office, 1990.

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United States. Congress. House. Committee on Government Operations. Government Activities and Transportation Subcommittee. Review of FAA procurement of airport surface detection equipment, ASDE-3: Hearing before the Government Activities and Transportation Subcommittee of the Committee on Government Operations, House of Representatives, One Hundred Second Congress, first session, July 10, 1991. U.S. G.P.O., 1992.

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Houghton, Rick. Emergency Characterization of Unknown Materials. Taylor and Francis, 2007.

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Houghton, Rick. Emergency characterization of unknown materials. CRC Press, 2008.

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Book chapters on the topic "Accident detector"

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Jafari, Shahram, Mohammad Arabnejad, and Ali Rashidi Moakhar. "Design and Implementation of a Fuzzy Accident Detector." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_135.

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Majid, Mohammad Anas, and Mohd Yousuf Ansari. "Accident Hotspot Detection." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6349-8_10.

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Bharadwaj, Rakhi, Manthan Tagad, Tejas Katkade, Aniket Ukarde, and Shritej Joshi. "Accident Detection Using Deep Learning." In Intelligent Sustainable Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1726-6_52.

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Adil, A. P., M. G. Anandhu, Jeovan Elsa Joy, Twinkle S. Karethara, S. Anjali, and B. R. Poorna. "Accident Detection in Surveillance Camera." In Intelligent Cyber Physical Systems and Internet of Things. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18497-0_26.

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Wong, Peter Kok-Yiu, Chin Pok Lam, Yin Ni Lee, Chung Lam Ting, Jack C. P. Cheng, and Pak Him Leung. "Predictive Safety Monitoring for Lifting Operations with Vision-Based Crane-Worker Conflict Prediction." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.64.

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Construction industry has reported among the highest accident and fatality rates over the past decade. In particular, crane lifting is a notably hazardous operation on construction sites, causing fatal accidents like workers being struck by the boom or objects fallen from tower cranes. Manual monitoring by on-site safety officers is labour-intensive and error-prone, while incorporating computer vision techniques into surveillance cameras would enable more automatic and continuous monitoring of construction site operations. However, existing studies for lifting safety mainly detect the presence of individual objects (e.g. workers, crane components), while a methodology is needed to predict their potential collision more proactively before accidents happen. This paper develops a vision-based framework for predictive lifting safety monitoring, including three modules: (1) object detection and classification: targeting at hook and lifting materials to enable danger zone estimation, along with workers and their personal protective equipment; (2) worker movement tracking and prediction: analyzing the historical moving trajectory of each unique worker to foresee his/her future movement in certain period ahead; (3) multi-level safety assessment: issuing predictive warning in real-time upon any crane-worker conflict foreseen. The proposed framework is applicable to real-time site video processing and enables end-to-end lifting safety monitoring with instant alerting upon unsafe scenarios observed. Importantly, the proposed framework predicts the future movement of workers to proactively identify potential site hazard, in order to trigger earlier safety alert for more timely decision-making. With a large video dataset capturing tower crane operations, the proposed framework demonstrates competitive accuracy and computational efficiency in crane-worker conflict prediction, validating its practicality for real-time lifting safety monitoring
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Wong, Peter Kok-Yiu, Chin Pok Lam, Yin Ni Lee, Chung Lam Ting, Jack C. P. Cheng, and Pak Him Leung. "Predictive Safety Monitoring for Lifting Operations with Vision-Based Crane-Worker Conflict Prediction." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.64.

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Construction industry has reported among the highest accident and fatality rates over the past decade. In particular, crane lifting is a notably hazardous operation on construction sites, causing fatal accidents like workers being struck by the boom or objects fallen from tower cranes. Manual monitoring by on-site safety officers is labour-intensive and error-prone, while incorporating computer vision techniques into surveillance cameras would enable more automatic and continuous monitoring of construction site operations. However, existing studies for lifting safety mainly detect the presence of individual objects (e.g. workers, crane components), while a methodology is needed to predict their potential collision more proactively before accidents happen. This paper develops a vision-based framework for predictive lifting safety monitoring, including three modules: (1) object detection and classification: targeting at hook and lifting materials to enable danger zone estimation, along with workers and their personal protective equipment; (2) worker movement tracking and prediction: analyzing the historical moving trajectory of each unique worker to foresee his/her future movement in certain period ahead; (3) multi-level safety assessment: issuing predictive warning in real-time upon any crane-worker conflict foreseen. The proposed framework is applicable to real-time site video processing and enables end-to-end lifting safety monitoring with instant alerting upon unsafe scenarios observed. Importantly, the proposed framework predicts the future movement of workers to proactively identify potential site hazard, in order to trigger earlier safety alert for more timely decision-making. With a large video dataset capturing tower crane operations, the proposed framework demonstrates competitive accuracy and computational efficiency in crane-worker conflict prediction, validating its practicality for real-time lifting safety monitoring
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Krzysztoń, Mateusz, Marcin Lew, and Michał Marks. "NAD: Machine Learning Based Component for Unknown Attack Detection in Network Traffic." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_4.

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AbstractDetection of unknown attacks is challenging due to the lack of exemplary attack vectors. However, previously unknown attacks are a significant danger for systems due to a lack of tools for protecting systems against them, especially in fast-evolving Internet of Things (IoT) technology. The most widely used approach for malicious behaviour of the monitored system is detecting anomalies. The vicious behaviour might result from an attack (both known and unknown) or accidental breakdown. We present a Net Anomaly Detector (NAD) system that uses one-class classification Machine Learning techniques to detect anomalies in the network traffic. The highly modular architecture allows the system to be expanded with adapters for various types of networks. We propose and discuss multiple approaches for increasing detection quality and easing the component deployment in unknown networks by known attacks emulation, exhaustive feature extraction, hyperparameter tuning, detection threshold adaptation and ensemble models strategies. Furthermore, we present both centralized and decentralized deployment schemes and present preliminary results of experiments for the TCP/IP network traffic conducted on the CIC-IDS2017 dataset.
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Yoshitake, Hiroshi, Jinyu Gu, and Motoki Shino. "Occluded Area Detection Based on Sensor Fusion and Panoptic Segmentation." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_66.

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AbstractDetecting occluded areas in a driving environment is crucial to preventing traffic accidents against hidden road agents coming out from such occluded areas. Our previous work proposed a novel detection method that can offer geometric information of the detected areas by utilizing camera and LiDAR sensor fusion. However, it had difficulty identifying individual areas formed by different objects without information about distinct objects. Thus, the objective of this study was to improve our previous methodology, and panoptic segmentation, which can distinguish between individual objects and offer semantic class labels of the object, was adopted to overcome the limitation. Evaluation results revealed that our proposed methodology could achieve satisfactory results in occlusion area detection and superior accuracy in estimating hidden road agent types in the detected areas.
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Quoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Conference papers on the topic "Accident detector"

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Anuradha, C., Siddheshwari B, Roshini S, and Srimathi M. "Automated Accident Detector Using Deep Learning." In 2025 International Conference on Data Science and Business Systems (ICDSBS). IEEE, 2025. https://doi.org/10.1109/icdsbs63635.2025.11031891.

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Khelifi, Amine, Mohamed Trabelsi, Lacey Thompson, Giuseppina Carannante, Nidhal Bouaynaya, and Charles Johnson. "Advancing Cockpit Safety: Cost-Effective Flight Data Monitoring with Deep Learning." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1239.

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The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading of each detected gauge. The contribution of this work is two-fold: (1) enhance rotorcraft safety by developing a DL framework capable of detecting, classifying, and inferring gauge readings for different helicopter types, and (2) boost research in the field by constructing a curated dataset valuable for aviation and machine learning communities.
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Pokkuluri, Kiran Sree, Manthena Sivanjani, M. Prasad, Raja Rao PBV, P. J. R. Shalem Raju, and Anuj Rapaka. "Deep Learning-Based Detection of Traffic Accidents Using CNN and VGG16 on Accident and Foggy Image Datasets." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969159.

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Divya, B. M., G. K. Ravikumar, and S. V. Shashikala. "Yolo in Accident Pattern Detection." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895341.

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Shaik, Zuber Basha, Jayakumar Dontabhaktuni, Saketi Bhavani, et al. "Autonomous Vehicle Accident Detection with Event Data Recording for Accident Analysis." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870686.

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Okae, Percy, and Hadijatu Mohamadu. "Car Accident Detection and Response System." In 2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST). IEEE, 2024. https://doi.org/10.1109/icast61769.2024.10856472.

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Rane, Milind, Avishkar Panaskar, Aryan Panindre, Neha Pansare, and Shreya Nikole. "Accident Detection System in GHAT Roads." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859608.

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Zhou, Ziyi. "Traffic accident detection based on YOLOv11." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898397.

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Katkar, Sunil, Sayali Gupta, Trupti Hedalkar, and Dheeraj Singh. "Vehicle Accident Detection and Alert System." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988317.

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Karumuri, N. P. R. Akash, Sumanth Nag Mandapalli, Shaik Ahammad Jani, et al. "Automatic Vehicle Accident Detection using IoT." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10993657.

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Reports on the topic "Accident detector"

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Dobelbower, M. C., J. Woollard, B. L. Jr Lee, and R. W. Jr Tayloe. Verification of criticality accident alarm system detector locations for the X-326 process cell floor. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/110805.

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Skapik, C. W., and B. L. Jr Lee. A comparison of two criticality accident alarm system detector locations for the X-700 building at the Portsmouth Gaseous Diffusion Plant. Final report. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/215842.

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McCallum and Richard. L52247 Human Factors Analysis of Leak Detection and Response Scoping Study. Pipeline Research Council International, Inc. (PRCI), 2004. http://dx.doi.org/10.55274/r0010251.

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There has been a substantial level of effort over the past several decades to understand the role of the human controller/operator in process control industry accidents. This work received a substantial impetus from the Three Mile Island nuclear reactor accident in March 1979, which led to an increased focus on the role of the operator as a decision-maker within the process control system. In response to this focus, industry guidelines were developed to address human factors issues in control room design and plant maintainability, among other issues. During the same period that industry guidelines were being developed in the nuclear industry, the human factors community made significant progress in developing theoretical models of the role of the operator in process control. Models that recognized the importance of operators" cognitive capacities, tendencies, and errors provided additional analytic power in both reconstructing accidents and developing approaches toward new designs. Two alternative near-term research plans were identified, along with the required funds, industry resources, and time. Expected products resulting from two alternative research efforts were outlined. An immediate benefit to industry in supporting the outlined research would be implementation of the Human Factors Operational Review Procedure. This would allow operators to come into compliance with key aspects of the Pipeline Integrity Management rules. More specifically, this guideline would be responsive to the requirement to identify and evaluate preventive and mitigative measures to protect High Consequence Areas, including emergency procedures for responding to spills and ruptures. Human factors critically affect operators" capability to detect and respond to spills, ruptures, and other emergency conditions; and the proposed guideline would directly support these requirements. More generally, it is anticipated that support of this research would result in significant improvement in operational safety, reliability, and efficiency.
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Kulhandjian, Hovannes. AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2127.

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In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present-day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian’s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS).
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Bäumler, Maximilian, and Matthias Lehmann. Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data. TU Dresden, 2024. http://dx.doi.org/10.26128/2024.2.

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Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample quality but low depth of information. However, for local video-based traffic observation (VO) data using drones and covering normal driving and critical situations, the opposite is true. Only the fusion of both sources of data using statistical matching can yield a representative, meaningful database able to generate representative test scenarios. For successful fusion, which requires as many relevant, shared features in both data sources as possible, the following question arises: How can VO data be collected by drones and analysed to create the maximum number of relevant, shared features with PD? To answer that question, we used the Find–Unify–Synthesise–Evaluation (FUSE) for Representativity (FUSE4Rep) process model.We applied the first (“Find”) and second (“Unify”) step of this model to VO data and conducted drone-based VOs at two intersections in Dresden, Germany, to verify our results. We observed a three-way and a four-way intersection, both without traffic signals, for more than 27 h, following a fixed sample plan. To generate as many relevant information as possible, the drone pilots collected 122 variables for each observation (which we published in the ListDB Codebook) and the behavioural errors of road users, among other information. Next, we analysed the videos for traffic conflicts, which we classified according to the German accident type catalogue and matched with complementary information collected by the drone pilots. Last, we assessed the crash risk for the detected traffic conflicts using generalised extreme value (GEV) modelling. For example, accident type 211 was predicted as happening 1.3 times per year at the observed four-way intersection. The process ultimately facilitated the preparation of VO data for fusion with PD. The orientation towards traffic conflicts, the matched behavioural errors and the estimated GEV allowed creating accident-relevant scenarios. Thus, the model applied to VO data marks an important step towards realising a representative test scenario database and, in turn, safe ADSs.
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Baker, James, and William Newmyr. ANS8.3 Criteria and the Use of Electronic Personal Detectors as a Criticality Accident Alarm System. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2204691.

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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Tayloe, R. W. Jr, A. S. Brown, M. C. Dobelbower, and J. E. Woollard. Determination of the response function for the Portsmouth Gaseous Diffusion Plant criticality accident alarm system neutron detectors. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/471366.

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Sakulneya, Apidej, and Jeffery Roesler. Smart Construction Work-Zone Safety with V2I Passive Material Sensing. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-027.

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This study explored new vehicle to infrastructure (V2I) technology in construction work zones (CWZ), where speeding, unsafe driving behaviors, and drivers' failure to obey traffic signs contribute significantly to elevated accident rates and fatalities. The objective of this research to advance CWZ safety by evaluating the potential of 3-axis magnetometers attached to a moving cart and traversing over a pavement-assisted passive sensing system can improve vehicle lateral positioning and warning in CWZ. Secondly, to develop a process to implement a programmable ferromagnetic oxide material for roadway coatings to interface with vehicles containing magnetometers on a field site. The research testing used a custom-built cart equipped with multiple 3-axis magnetometer to detect EM signals from invisible markings composed of 10% and 20% CrO₂, that were created to alert for speed, lane merges, and lane-keeping. The invisible marking strips were oriented and positioned in various ways to test the repeatability and ability to reliable detect a signal and signature that could be interpreted with automated algorithm. The experimental test results were acquired in a parking and signal-processing technique was established that normalized the raw signals, removed background EM signals not related to the created EM signatures, filtered high- and low-frequency noise, and took the derivative of the EM flux density with respect to the number of points. The V2I signals in the Y and Z-axes occasionally failed to exceed the minimum threshold set for the experiments, but the X-axis signals consistently exceeded the minimum value of ±200nT throughout the testing. The minimum threshold signals were used to calculate the speed of the cart, indicate a lane merge, and determine the lateral lane position of the cart. The detected speed signals closely correlated with the GPS speed measurements on the cart as well as provided accurate cart positioning and maneuvering actions. This pilot study demonstrated the potential of V2I communication specifically EM pavement signatures to enhance CWZ safety and provide detectable and actionable feedback to the vehicle.
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Stabilini, Alberto, David Breitenmoser, Federico Geser, et al. Aeroradiometric measurements in the framework of the Swiss ARM24 and international AGC24 exercises. Paul Scherrer Institute, PSI, 2025. https://doi.org/10.55402/psi:68900.

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Measurements of the civil exercise (ARM24c) took place at the Bürgenstock on the 29th May and on the 29th and 31st May in the area of the Beznau and Leibstadt nuclear power plants, the Zwilag interim storage facility, and the research facilities of the Paul Scherrer Institute. Atmospheric and terrestrial short-lived anthropogenic radionuclides in the western area of the Paul Scherrer Institute were detected and quantified using a newly developed Monte Carlo based full-spectrum Bayesian inversion method. A modest contribution of 16N was detected above the premises of the nuclear power plant of Leibstadt (KKL), which was resuming operations. These emissions are allowed and monitored by the competent authorities. No deviations from the natural background were detected in the vicinities of the nuclear installation of the nuclear power plant of Beznau (KKB) and the interim storage facility Zwilag. The military campaign (ARM24m) surveyed the areas of Fleurier, Sainte-Croix, Concise, Ecuvillens, Gibloux in the western part of Swizterland between September the 17th and September the 20th. Traces of the Chernobyl accident depositions were detected in the Jura heights. The international campaign (AGC24) organised by the Czech Republic was held in Prerov (CZ) from the 3rd to the the 7th of June. The exercise involved reference measurements in designated areas characterised with in-situ measurements, the search and identification of radioactive sources, the assessment of a radiologically significant region with uranium-rich soil and subsoil, and composite mapping. Such exercises provide valuable opportunities to compare and refine methodologies, practise measurements under unique conditions not present in the national territory but pertinent to emergency scenarios, and harmonise procedures and data formats. These efforts contribute to smoother and more efficient international support during radiological emergencies.
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