Spatiotemporal Feature Enhancement Aids the Driving Intention Inference of Intelligent Vehicles

Huiqin Chen (), Hailong Chen, Hao Liu and Xiexing Feng
Additional contact information
Huiqin Chen: College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Hailong Chen: College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Hao Liu: College of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Xiexing Feng: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 2T6, Canada

IJERPH, 2022, vol. 19, issue 18, 1-17

Abstract: In order that fully self-driving vehicles can be realized, it is believed that systems where the driver shares control and authority with the intelligent vehicle offer the most effective solution. An understanding of driving intention is the key to building a collaborative autonomous driving system. In this study, the proposed method incorporates the spatiotemporal features of driver behavior and forward-facing traffic scenes through a feature extraction module; the joint representation was input into an inference module for obtaining driver intentions. The feature extraction module was a two-stream structure that was designed based on a deep three-dimensional convolutional neural network. To accommodate the differences in video data inside and outside the cab, the two-stream network consists of a slow pathway that processes the driver behavior data with low frame rates, along with a fast pathway that processes traffic scene data with high frame rates. Then, a gated recurrent unit, based on a recurrent neural network, and a fully connected layer constitute an intent inference module to estimate the driver’s lane-change and turning intentions. A public dataset, Brain4Cars, was used to validate the proposed method. The results showed that compared with modeling using the data related to driver behaviors, the ability of intention inference is significantly improved after integrating traffic scene information. The overall accuracy of the intention inference of five intents was 84.92% at a time of 1 s prior to the maneuver, indicating that making full use of traffic scene information was an effective way to improve inference performance.

Keywords: driving intention inference; two-stream networks; spatiotemporal features; intelligent vehicle (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/18/11819/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/18/11819/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:18:p:11819-:d:918862

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().