HighSafe | Algotechniq

Objective

The goal of this project is to develop an AI-enabled accident detection and alert system specifically designed for smart cities. The system integrates IoT (Internet of Things) with deep learning techniques to enhance accident detection accuracy and improve response times for rescue operations.

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Tracking highway traffic in Tokyo.

Prior Work

Accident detection and alert systems have previously relied on simpler technologies such as GPS data, accelerometers, and basic machine learning algorithms (ElSahly & Abdelfatah, 2022). However, they often had limitations such as dependency on a single sensor, high false alarm rates, and lack of comprehensive integration with emergency services.

Benefits

The new approach benefits city authorities, emergency services (like hospitals and police stations), vehicle owners, and the general public. By providing real-time, accurate accident detection and immediate alerting of relevant services, the system can significantly reduce response times, potentially saving lives and reducing the severity of injuries from road accidents.

Enabling Technologies

The key enabling technologies include:

Deep Learning: Pre-trained models like EfficientNet and Vision Transformers (ViT) are used to analyze accident data and minimize false detection rates.
Cloud Computing: Used for processing and storing data and for running deep learning models.
Communication Modules: 5G and Wi-Fi modules to transmit data and alerts to emergency services.

Key Performance Indicators

The success of the new approach can be quantified using various performance metrics, including:

Accuracy of Accident Detection: Measured through precision, recall, and F1 score of the deep learning models.
False Alarm Rate: The system’s ability to minimize false positives in accident detection.
Response Time: The time taken to alert emergency services after an accident is detected.

References

2022

A Systematic Review of Traffic Incident Detection Algorithms

Osama ElSahly, and Akmal Abdelfatah

Sustainability, 2022

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Traffic incidents have negative impacts on traffic flow and the gross domestic product of most countries. In addition, they may result in fatalities and injuries. Thus, efficient incident detection systems have a vital role in restoring normal traffic conditions on the roads and saving lives and properties. Researchers have realized the importance of Automatic Incident Detection (AID) systems and conducted several studies to develop AID systems to quickly detect traffic incidents with an acceptable performance level. An incident detection system mainly consists of two modules: a data collection module and a data processing module. The performance of AID systems is assessed using three performance measures; Detection Rate (DR), False Alarm Rate (FAR) and Mean Time to Detect (MTTD). Based on data processing and incident detection algorithms, AID can be categorized into four categories: comparative, statistical, artificial intelligence-based and video–image processing algorithms. The aim of this paper is to investigate and summarize the existing AID systems by assessing their performance, strengths, limitations and their corresponding data collection and data processing techniques. This is useful in highlighting the shortcomings of these systems and providing potential solutions that future research should focus on. The literature is sought through an extensive review of the existing refereed publications using the Google Scholar search engine and Scopus database. The methodology adopted for this research is a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study can serve as a reference for researchers who are interested in developing new AID systems.