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. 2021 Dec 20;21(24):8498.
doi: 10.3390/s21248498.

Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Lei Yang  1 , Chunqing Zhao  2 , Chao Lu  1 , Lianzhen Wei  1   3 , Jianwei Gong  1   3
Affiliations

Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Lei Yang et al. Sensors (Basel). .

Abstract

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-_target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver's operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

Keywords: deep belief network; driving behavior prediction; intelligent vehicles.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed driving behavior prediction system.
Figure 2
Figure 2
The typical DBN driving behavior prediction architecture.
Figure 3
Figure 3
Improved MSR-DBN prediction model.
Figure 4
Figure 4
Schematic diagram for the RBM and training process for the pre-training.
Figure 5
Figure 5
Data acquisition route.
Figure 6
Figure 6
Prediction errors for surrounding vehicles based on different methods.
Figure 7
Figure 7
Prediction results of the front wheel angle based on different methods.
Figure 8
Figure 8
Prediction results of the speed based on different methods.
Figure 9
Figure 9
Prediction results on highD dataset. (a) Prediction result of lateral speed. (b) Prediction result of longitudinal speed.
See this image and copyright information in PMC

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