Transfer Learning for Driving Pattern Recognition

Maoying Li, Liu Yang, Qinghua Hu, Chenyang Shen, Zhibin Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Driving pattern recognition based on driving status features (GPS, gear, and speed etc.) is of central importance in the development of intelligent transportation. While it is expensive and labor intensive to obtain a large amount of labeled driving data in real applications. It makes the driving pattern recognition particularly difficult for those domains without labeled data. In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that can transfer knowledge from other related source domains with labeled data to the target domain. Compared to the traditional supervised learning, one of the major difficulties of transfer learning is that the data from different domains may have distinct distributions. The proposed TLDPR is able to reduce the distribution difference in RKHS between the samples in target and source domain with the same driving pattern, and it can preserve the local manifold structure simultaneously. In addition, an iterative ensemble strategy is implemented to make the model more robust using the pseudo-labels. To evaluate the performance of TLDPR, comprehensive experiments have been conducted on parking lots datasets. The results show TLDPR can substantially outperform the state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationPRICAI 2019
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030299101
StatePublished - 2019
Event16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
Duration: Aug 26 2019Aug 30 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11671 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
CityYanuka Island


  • Driving pattern
  • Maximum mean discrepancy
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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