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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Spatiotemporal Feature Enhancement Aids the Driving Intention Inference of Intelligent Vehicles.

Huiqin Chen¹, Hailong Chen¹, Hao Liu¹

¹College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.

International Journal of Environmental Research and Public Health

|September 23, 2022

Summary

Understanding driver intention is key for self-driving cars. Integrating traffic scene data with driver behavior significantly improves the accuracy of predicting driving intentions like lane changes and turns.

Keywords:

driving intention inference intelligent vehicle spatiotemporal features two-stream networks

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Area of Science:

Intelligent transportation systems
Human-computer interaction
Computer vision

Background:

Realizing fully self-driving vehicles requires effective human-vehicle collaboration.
Understanding driver intention is crucial for developing shared control systems in autonomous driving.

Purpose of the Study:

To develop a method for inferring driver intentions by integrating spatiotemporal features of driver behavior and traffic scenes.
To improve the accuracy of intention prediction for collaborative autonomous driving systems.

Main Methods:

A two-stream deep three-dimensional convolutional neural network (3D CNN) was used for feature extraction.
A slow pathway processed driver behavior data (low frame rate), and a fast pathway processed traffic scene data (high frame rate).
A gated recurrent unit (GRU) and a fully connected layer formed the intent inference module to predict lane-change and turning intentions.

Main Results:

Integrating traffic scene information significantly improved intention inference compared to using only driver behavior data.
The proposed method achieved an overall accuracy of 84.92% for inferring five types of intentions, 1 second before the maneuver.
The study demonstrated the effectiveness of leveraging traffic scene information for enhanced inference performance.

Conclusions:

Combining driver behavior and traffic scene data is an effective strategy for improving driver intention inference in autonomous driving systems.
The proposed deep learning approach provides a robust method for predicting driver intentions, crucial for safe and efficient human-vehicle collaboration.