Citation

Phanomchoeng G, Treetipsounthorn K, Chantranuwathana S, Wuttisittikulkij L. Int. J. Automot. Technol. 2023; 24(3): 811-828.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12239-023-0067-9

PMID

unavailable

Abstract

To improve rollover prevention and rollover warning systems, indicators for detecting rollover risks are extremely important. Vehicle rollover accidents occur in one of two ways: tripped and untripped rollovers. For detecting tripped rollovers, the traditional rollover index is ineffective; most precise rollover indicators depend on dynamic models that must identify all the parameters for computations. In this study, we focused on exploring a new index for detecting tripped and untripped rollovers using a neural network (NN). Four types of NNs, i.e., FNN, Tanh, long short-term memory, and gated recurrent unit (GRU), were examined to develop models for estimating rollover indices. The results demonstrated that the GRU and large Tanh network are the most suitable NNs for untripped and tripped rollover prediction, respectively. Moreover, the untripped rollover prediction model having a small GRU network could precisely anticipate the trend of the untripped rollover indicators for up to 0.2 s in advance. Moreover, the created tripped rollover anticipation model with a large Tanh network could precisely forecast the trend of the tripped rollover index up to 0.5 s in advance. Based on these results, rollover prediction in future can be advantageous for rollover prevention and warning systems.

Language: en

Keywords

Feedforward neural network; gated recurrent unit (GRU); LSTM; Recurrent neural network; Rollover index; Tanh; Tripped rollover; Untripped rollover