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

Lei Yang¹, Chunqing Zhao², Chao Lu¹

¹School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This study introduces a novel deep belief network (DBN) for predicting intelligent vehicle driving behaviors, enhancing safety. The MSR-DBN model accurately forecasts front wheel angle and speed, outperforming existing methods.

Keywords:

deep belief network driving behavior prediction intelligent vehicles

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Area of Science:

Intelligent Transportation Systems
Machine Learning for Autonomous Driving
Deep Learning Architectures

Background:

Accurate prediction of human driving behavior is crucial for the safety of intelligent vehicles.
Existing models often struggle to capture the complex dynamics of real-world driving scenarios.

Purpose of the Study:

To propose a novel deep belief network (DBN) model, MSR-DBN, for predicting the front wheel angle and speed of an ego vehicle.
To enhance the accuracy and robustness of driving behavior prediction in intelligent vehicles.

Main Methods:

Developed a Multi-Target Sigmoid Regression Deep Belief Network (MSR-DBN) with two sub-networks for front wheel angle and speed prediction.
Incorporated historical states of the ego vehicle, surrounding vehicles, and driver operations as input features.
Employed a systematic testing method for optimizing lateral and longitudinal behavior predictions.

Main Results:

The MSR-DBN model demonstrated superior prediction accuracy and robustness compared to general DBN, BP, SVR, and RBF neural networks.
The model effectively predicts both lateral (front wheel angle) and longitudinal (speed) driving behaviors.

Conclusions:

The proposed MSR-DBN model offers a significant advancement in predicting driving behaviors for intelligent vehicles.
This approach contributes to enhanced safety and performance of autonomous driving systems.