An Intelligent Smartphone-Based Road Accident Detection and Emergency Alert System Using Multi-Sensor Data Fusion and Machine Learning

Abstract

In the world, road traffic accidents are among the top causes of fatalities: There is a large risk of severe injuries and fatalities if an emergency response is late. This paper introduces an intelligent road accident detection and emergency alert system for a smartphone which is based on the multi-sensor data fusion and machine-learning techniques that allow the fast detection of the accident and early alert notification. The proposed framework uses the data from the vehicle\'s accelerometer, gyroscope and Global Positioning System (GPS) to continuously monitor the specific dynamics of the vehicle, recognizing the abnormal patterns of movement involved in road accidents. The sensor noise is eliminated in a preprocessing step, and discriminative motion features are extracted from the sensor signals, which are then classified by a Support Vector Machine (SVM) to discriminate between the collision and normal driving events and minimize false alarms. In the case of a potential accident being detected, the system activates a reprogrammable confirmation timer the user can use to cancel unintentional alerts before automatically sending the location of the accident as well as emergency information to preprogrammed contacts. The proposed method does not require any special in-vehicle hardware, and uses inexpensive sensors from existing smartphones, which are also widely available, so it is a cost-effective and readily deployable solution. The proposed framework is evaluated through experimentation, and the results show a high accuracy of detection with a low false-positive rate, while remaining real-time for practical implementation. Intelligent sensor fusion, machine learning-based classification, and automated emergency communication contribute to an enhanced road safety, minimizing emergency response time and improving the reliability of accident detections.

Introduction

Road traffic accidents remain a major global problem, causing significant loss of life and injuries due to delayed accident detection and emergency response. Victims in remote areas or situations with limited assistance may not be able to contact emergency services immediately, increasing the severity of outcomes. This research focuses on developing an intelligent smartphone-based accident detection and emergency alert system using built-in sensors and machine learning.

Traditional accident detection approaches often rely on fixed thresholds, IoT devices, or vehicle-mounted hardware. While effective, these methods can generate false alarms due to sudden braking, rough roads, or phone movements and may require costly installations. The proposed system overcomes these limitations by using smartphone sensor data fusion and machine learning, eliminating the need for additional vehicle equipment.

The proposed framework continuously collects data from smartphone accelerometers, gyroscopes, and GPS sensors. The collected signals undergo preprocessing, including noise filtering, normalization, and time-window segmentation. Important motion features such as mean, RMS, standard deviation, peak acceleration, angular velocity, and jerk are extracted from the sensor data. These features are then analyzed using a Support Vector Machine (SVM) classifier to distinguish between genuine accidents and normal driving conditions.

The system architecture consists of six main modules:

1. Sensor Data Acquisition – Collects acceleration, rotation, speed, and location data from smartphone sensors.
2. Signal Preprocessing – Removes noise and prepares data for analysis.
3. Feature Extraction – Converts raw sensor signals into meaningful motion characteristics.
4. Accident Classification – Uses SVM to classify events as accidents or normal driving.
5. Decision Validation – Provides a user confirmation period to cancel false alarms.
6. Emergency Notification – Automatically sends accident details and GPS location to registered emergency contacts if no response is received.

The literature review shows that earlier threshold-based systems were simple but suffered from false detections. Multi-sensor approaches improved reliability, while machine learning methods such as SVM, Random Forest, Decision Trees, CNNs, and LSTMs provided better classification accuracy. However, deep learning models often require large datasets and high computational power, making lightweight machine learning approaches more suitable for smartphones.

The proposed SVM-based system provides a low-cost, scalable, and real-time accident detection solution suitable for both urban and rural environments. By combining sensor fusion, machine learning classification, and automated emergency communication, the system aims to reduce false alarms, improve accident recognition accuracy, and shorten emergency response time. Experimental evaluation using normal driving and simulated accident data measures performance using accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the approach.

Conclusion

The proposed paper was an intelligent road traffic accident detection and notification system using embedded sensors for the detection, signal processing and machine learning approach to enhance the accuracy of accident detection and emergency notification. The proposed framework exploits data obtained by the accelerometer, gyroscope, and GPS sensors onboard the smartphone to continuously monitor the vehicle dynamics and detect collision events in real-time. The use of signal preprocessing and statistical feature extraction enhances the quality of the sensor data obtained, and the Support Vector Machine (SVM) classification is used to better separate genuine accidents from normal driving conditions, thus minimizing false-positive classifications. Moreover, the provision of user confirmation prior to sending out emergency alerts creates greater reliability in the system, as it avoids sending unnecessary messages. The proposed framework is made possible in a commercially available smartphone, thereby not requiring any special vehicle equipment and also making it both cost effective, portable and scalable to enhance safety on roadways. The results shows the high detection accuracy, good classification performance and rapid emergency communication of the proposed method, and it has potential application practice value in the construction of intelligent transportation systems. Further study is planned to strengthen and make the proposed accident detecting system more intelligent by incorporating more sensors for smartphones and developing more intelligent deep learning model for the better recognition of accidents. The combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can be used to further improve the classification performance, as they can be trained on complex temporal patterns of sensor data. The framework can also be linked to the emergency response system that is cloud based for the hospitals, ambulance services and traffic management centers in the area, enabling earlier response and rescue. In addition, future iterations of the system might include additional features like driver behavior analysis, road conditions monitoring, and fatigue detection, providing comprehensive support for road safety. Fifth generation (5G) communication networks and Internet of Things (IoT) technologies can also further shorten emergency response time and bolster trust in real-time accident reporting in smart transportation system environments.

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Copyright

Copyright © 2026 Jaladi Sravanthi, Bolla Bhagya Lakshmi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.