Possibilities of vehicle state estimation using big data approaches

Dániel Fényes, Balázs Németh, Péter Gáspár, Máté Asszonyi

Research output: Contribution to conferencePaper

Abstract

Nowadays, the development of the autonomous vehicles is one of the most important challenges for the automotive industry. In general, these vehicles are equipped with numerous sensors, such as onboard-camera, lidar, radar, ultrasonic sensor, accelerometer and gyroscope. These sensors provide information about the environment and other vehicles. This information is not only processed and used directly on the car but also can be stored on internal or cloud-based memory. This collected data might contain hidden information about the motion and the dynamical behaviour of the car. This hidden information can be brought out of the datasets by using big data-based data mining approaches. The aim of the paper is to create a vehicle state estimation model based on the collected sensor data and using data mining tools. In the paper, the sensor data is collected from a high-fidelity simulation software, CarSim. The estimation model is created by the machine-learning software Weka. Finally, the created model is validated through several CarSim simulations.

Original languageEnglish
Pages395-401
Number of pages7
Publication statusPublished - Jan 1 2019
Event16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018 - , Hungary
Duration: Nov 5 2018Nov 7 2018

Conference

Conference16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018
CountryHungary
Period11/5/1811/7/18

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Keywords

  • Control-oriented modeling
  • Independent steering
  • Variable-geometry suspension

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

Fényes, D., Németh, B., Gáspár, P., & Asszonyi, M. (2019). Possibilities of vehicle state estimation using big data approaches. 395-401. Paper presented at 16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018, Hungary.