TY - GEN
T1 - Vehicle rollover detection using recurrent neural networks
AU - Dengler, Christian
AU - Treetipsounthorn, Kailerk
AU - Chantranuwathana, Sunhapos
AU - Phanomchoeng, Gridsada
AU - Lohmann, Boris
AU - Panngum, Setha
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Rollover accidents have a higher fatality rate than other types of accidents. Therefore, rollover prevention systems are of great importance for driver safety. The implementation of rollover prevention systems requires an estimation of the rollover risk. To assess that risk, different rollover indices have been introduced. A difficulty is the dependence of these indices on unknown parameters, e.g., center of gravity and current load of the vehicle. One solution is to implement an algorithm for the estimation of the required parameters that can be online measured. In this work however, we investigate the use of recurrent neural networks for the estimation of the rollover index. Their ability to work on sequential data is promising for a data based estimation without the need of an additional estimation algorithm. We implement and test different recurrent neural network architectures and compare the results with the achievable performance of a static neural network. The results are validated in simulation in the industry standard software CarSim.
AB - Rollover accidents have a higher fatality rate than other types of accidents. Therefore, rollover prevention systems are of great importance for driver safety. The implementation of rollover prevention systems requires an estimation of the rollover risk. To assess that risk, different rollover indices have been introduced. A difficulty is the dependence of these indices on unknown parameters, e.g., center of gravity and current load of the vehicle. One solution is to implement an algorithm for the estimation of the required parameters that can be online measured. In this work however, we investigate the use of recurrent neural networks for the estimation of the rollover index. Their ability to work on sequential data is promising for a data based estimation without the need of an additional estimation algorithm. We implement and test different recurrent neural network architectures and compare the results with the achievable performance of a static neural network. The results are validated in simulation in the industry standard software CarSim.
KW - Neural Network
KW - Recurrent Neural Network
KW - Rollover Detection
KW - Rollover Prevention
UR - http://www.scopus.com/inward/record.url?scp=85085862165&partnerID=8YFLogxK
U2 - 10.1109/CIS-RAM47153.2019.9095843
DO - 10.1109/CIS-RAM47153.2019.9095843
M3 - Conference contribution
AN - SCOPUS:85085862165
T3 - Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics, CIS and RAM 2019
SP - 59
EP - 64
BT - Proceedings of the IEEE 2019 9th International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics, CIS and RAM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Cybernetics and Intelligent Systems and Robotics, Automation and Mechatronics, CIS and RAM 2019
Y2 - 18 November 2019 through 20 November 2019
ER -