Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

2026-04-17

AI Summary

This study addresses the adverse impact of ambulance-induced vibrations during transit, which can interfere with medical procedures and compromise patient care. To mitigate this issue, the authors propose an intelligent driving assistance system that integrates vibration awareness with route planning. The system collects road-induced vibration data using accelerometers and GPS, and employs a neural network to classify vibration severity with 97% accuracy. For the first time, quantified vibration metrics are incorporated into a route optimization model that prioritizes smoother paths while maintaining timely arrival. Experimental results demonstrate that when alternative routes differ in travel time by less than 6%, the system reliably recommends paths with significantly reduced vibration levels, thereby enhancing patient safety without sacrificing urgency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.

Problem

• ambulance
• vibration reduction
• driving assistance
• patient safety
• route planning

Innovation

• ambulance driving assistance
• vibration minimization
• artificial neural network
• route recommendation
• patient cabin comfort