Authors:
Mojca Komavec, Boštjan Kaluža, Kristina Stojmenova, Jaka Sodnik
Keywords:
driving simulator, driver evaluation, risk assessment, neural networks
Abstract:
Driving simulators can be used for training and evaluation of driving skills. However, for an effective training process, a scoring system with relevant feedback should be applied. Such scoring system can be then used for providing feedback on lacking skills for customized training or for estimation of potential risky driving behaviour. In this paper, we present a new approach for evaluation of the driver, which is based on data obtained through a simulated driving session with variety of challenging roads and traffic conditions and data acquired from biometrical sensors. First, we present a rule-based scoring model for driver evaluation, which predicts the likelihood of a driver being involved in risky behaviour. In the second part we present a process of recalibration of weights of the scoring model using neural networks and Naïve Bayes algorithm. The results showed that neural networks outperformed all of other tested models, as it achieved 23% better results in terms of classification accuracy, and 7% better results in terms of precision when compared to the initial rule-based model. However, by using machine learning approach, the interpretation of final score and weights is very limited and valuable feedback on lacking skills is lost. We evaluate such scoring systems as appropriate for risk assessment, while rule-based models prove to be more appropriate for driver training.
Komavec M.; Kaluža B.; Stojmenova K. and Sodnik J. Risk Assessment Score Based on Simulated Driving Session In: Proceedings of the Driving Simulation Conference 2019 Europe VR, Driving Simulation Association, Strasbourg, France, 2019, pp. 67-74
Download .txt file
@inproceedings{Komavec2019,
title = {Risk Assessment Score Based on Simulated Driving Session},
author = {Mojca Komavec and Boštjan Kaluža and Kristina Stojmenova and Jaka Sodnik},
editor = {Andras Kemeny and Florent Colombet and Frédéric Merienne and Stéphane Espié},
isbn = {978-2-85782-749-8},
year = {2019},
date = {2019-09-04},
booktitle = {Proceedings of the Driving Simulation Conference 2019 Europe VR},
pages = {67-74},
address = {Strasbourg, France},
organization = {Driving Simulation Association},
abstract = {Driving simulators can be used for training and evaluation of driving skills. However, for an effective training process, a scoring system with relevant feedback should be applied. Such scoring system can be then used for providing feedback on lacking skills for customized training or for estimation of potential risky driving behaviour. In this paper, we present a new approach for evaluation of the driver, which is based on data obtained through a simulated driving session with variety of challenging roads and traffic conditions and data acquired from biometrical sensors. First, we present a rule-based scoring model for driver evaluation, which predicts the likelihood of a driver being involved in risky behaviour. In the second part we present a process of recalibration of weights of the scoring model using neural networks and Naïve Bayes algorithm. The results showed that neural networks outperformed all of other tested models, as it achieved 23% better results in terms of classification accuracy, and 7% better results in terms of precision when compared to the initial rule-based model. However, by using machine learning approach, the interpretation of final score and weights is very limited and valuable feedback on lacking skills is lost. We evaluate such scoring systems as appropriate for risk assessment, while rule-based models prove to be more appropriate for driver training.},
keywords = {driver evaluation, driving simulator, neural networks, risk assessment},
}
Download .bib file
TY - CONF
TI - Risk Assessment Score Based on Simulated Driving Session
AU - Komavec, Mojca
AU - Kaluža, Boštjan
AU - Stojmenova, Kristina
AU - Sodnik, Jaka
C1 - Strasbourg, France
C3 - Proceedings of the Driving Simulation Conference 2019 Europe VR
DA - 2019/09/04
PY - 2019
SP - 67
EP - 74
LA - en-US
PB - Driving Simulation Association
SN - 978-2-85782-749-8
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2019/risk-assessment-score-based-on-simulated-driving-session
ER -
Download .ris file
Cite this article
Terms and Conditions for Downloading Driving Simulation Proceedings papers:
By downloading a scientific paper from proceedings.driving-simulation.org, you agree to the following terms and conditions:
- Personal Use Only:
The scientific paper provided on this website is solely for personal, educational, and non-commercial use. You may download and use the paper for your own reference and research purposes only.
- No Reproduction or Distribution:
You may not reproduce, distribute, transmit, publish, or otherwise make the paper available to any third party in any form, whether for commercial or non-commercial purposes, without the express written consent of the Driving Simulation Association.
- Copyright and Ownership:
The scientific paper is protected by copyright laws and is the intellectual property of the respective authors and publishers. All rights not expressly granted herein are reserved.
- Citation and Attribution:
If you use the scientific paper for research, presentations, or any other non-commercial purposes, you must provide appropriate citation and attribution to the original authors as per academic standards.
- No Modification:
You may not modify, alter, or adapt the content of the scientific paper in any way.
- Disclaimer:
The Driving Simulation Association makes no representations or warranties regarding the accuracy, completeness, or suitability of the scientific paper for any particular purpose. The paper is provided as-is, without any warranties, express or implied. The Driving Simulation Association reserves the right to terminate or restrict access to the scientific paper at any time and without notice.