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

• URL: http://arxiv.org/abs/2604.16047v1
• Date: Fri, 17 Apr 2026 13:21:05 GMT
• Title: Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin
• Abstract summary: We have trained, validated, and tested a system to assess driving in ambulance services.<n>The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer.<n>It also includes a GPS sensor, a battery, a display, and a speaker.
• Score: 4.7106603037973045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring [69.91458772455071]
  emphSkyDrive is a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving agents in a new environment.<n>Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities.
  arXiv  Detail & Related papers  (2026-08-25T20:47:09Z)
• Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026 [39.381642096565834]
  The dataset provides individual vehicle speed observations on European E-roads tagged in OpenStreetMap as e-road, for the years 2022-2026.<n>Speeds are derived from Copernicus Sentinel-2 Level-2A satellite optical imagery using a processing pipeline.<n>The dataset is distributed as GeoPackage files, with one record per detected vehicle, and can support studies of traffic patterns, speed behavior, transport modeling, and the calibration of road network attributes at a continental scale.
  arXiv  Detail & Related papers  (2026-08-22T21:59:36Z)
• A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring without Lane Closure [0.0]
  This paper presents a Unity-based digital twin framework for traffic-aware UAV pavement monitoring without lane closure.<n>The proposed environment integrates procedurally generated road defects, dynamic vehicles and pedestrians, autonomous UAV navigation, and an embedded road-damage perception pipeline.<n>The digital twin was then used to evaluate three recovery strategies for occluded road segments: hover-and-recheck, micro-repositioning, and skip-and-revisit.
  arXiv  Detail & Related papers  (2026-06-17T20:30:52Z)
• Low-Cost System for Automatic Recognition of Driving Pattern in Assessing Interurban Mobility using Geo-Information [4.318840065508993]
  We propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving.<n>The system consists of two physical sensors connected to a device node with a display and a speaker.<n>An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style.
  arXiv  Detail & Related papers  (2026-04-16T16:47:08Z)
• Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods [51.28632782308621]
  We focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group.<n>We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM)<n>Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle.
  arXiv  Detail & Related papers  (2025-07-01T09:20:41Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
  On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm. We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
  arXiv  Detail & Related papers  (2024-06-30T04:21:46Z)
• Learning Position From Vehicle Vibration Using an Inertial Measurement Unit [2.1213500139850012]
  This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS) Traditional approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low reception areas. This study proposes a vehicle positioning method based on learning the road signature from accelerometer and gyroscope measurements obtained by an inertial measurement unit (IMU) sensor.
  arXiv  Detail & Related papers  (2023-03-06T18:55:00Z)
• Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks [67.60224656603823]
  This paper proposes the development of a railway axle condition monitoring system based on advanced 2D-Convolutional Neural Network (CNN) architectures. The resultant system converts the railway axle vibration signals into time-frequency domain representations, i.e., spectrograms, and, thus, trains a two-dimensional CNN to classify them depending on their cracks.
  arXiv  Detail & Related papers  (2022-11-15T15:04:06Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• E-Scooter Rider Detection and Classification in Dense Urban Environments [5.606792370296115]
  This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art.
  arXiv  Detail & Related papers  (2022-05-20T13:50:36Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• Destination Prediction Based on Partial Trajectory Data [4.783019576803369]
  Two-thirds of people who buy a new car prefer to use a substitute instead of the built-in navigation system. For many applications, knowledge about a user's intended destination and route is crucial. Our approach predicts probable destinations and routes of a vehicle, based on the most recent partial trajectory and additional contextual data.
  arXiv  Detail & Related papers  (2020-04-16T06:26:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.