Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques
AbstractDriver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Kim, I.-H.; Bong, J.-H.; Park, J.; Park, S. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques. Sensors 2017, 17, 1350.
Kim I-H, Bong J-H, Park J, Park S. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques. Sensors. 2017; 17(6):1350.Chicago/Turabian Style
Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk. 2017. "Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques." Sensors 17, no. 6: 1350.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.