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. 2017 Jun 10;17(6):1350.
doi: 10.3390/s17061350.

Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Il-Hwan Kim  1 , Jae-Hwan Bong  2 , Jooyoung Park  3 , Shinsuk Park  4
Affiliations

Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Il-Hwan Kim et al. Sensors (Basel). .

Abstract

Driver 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.

Keywords: advanced driver assistance system (ADAS); artificial neural network (ANN); driver’s intention; lane change; support vector machine (SVM).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A schematic diagram of the system developed for driver intention classification.
Figure 2
Figure 2
Vehicle states measured by on-board sensors and estimated by ANN models.
Figure 3
Figure 3
Basic network architecture of three-layered ANN.
Figure 4
Figure 4
Artificial neural network model for road condition classification module.
Figure 5
Figure 5
NARX Neural network model for estimation of vehicle state parameters (z−1 is the unit time delay).
Figure 6
Figure 6
Use of feature map for non-separable problem.
Figure 7
Figure 7
Operation procedure of driver intention recognition using SVM.
Figure 8
Figure 8
Setup for driving simulator experiments: (a) schematic diagram of driving simulator; and (b) setup of Steering wheel and pedal for obtaining driving data.
Figure 9
Figure 9
Classification of the road condition while the throttle is on.
Figure 10
Figure 10
Estimated Lateral Velocity depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snow.
Figure 11
Figure 11
Estimated Side slip angle depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snowy.
Figure 12
Figure 12
Estimated Lateral Tire Force depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snowy.
Figure 13
Figure 13
Estimated Roll rate depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snowy.
Figure 14
Figure 14
Estimated Suspension Spring Compression depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snowy.
Figure 15
Figure 15
Estimated Heading (Yaw) depending on road surface condition: (a) Dry asphalt; (b) Gravel; (c) Wet; and (d) Snowy.
Figure 16
Figure 16
Lane change maneuvers and driver’s intention: (a) steering wheel angle; and (b) driving state (−1 is LCR, 0 is LK, 1 is LCL).
See this image and copyright information in PMC

References

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