Structured behaviour prediction of on-road vehicles via deep forest

Luntian Mou; Shasha Mao; Haitao Xie; Yanyan Chen

Structured behaviour prediction of on-road vehicles via deep forest

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Author(s): Luntian Mou¹ ; Shasha Mao² ; Haitao Xie¹ ; Yanyan Chen¹
- Affiliations: 1: Beijing Key Laboratory of Traffic Engineering , Beijing University of Technology , Beijing , People's Republic of China ;
  2: School of Artificial Intelligence , Xidian University , Xi'an , People's Republic of China
Source: Volume 55, Issue 8, 18 April 2019, p. 452 – 455
DOI: 10.1049/el.2019.0472 , Print ISSN 0013-5194, Online ISSN 1350-911X

Received 05/02/2019, Published 21/03/2019

Vision-based vehicle behaviour analysis has drawn increasing research efforts as an interesting and challenging issue in recent years. Although a variety of approaches have been taken to characterise on-road behaviour, there still lacks a general model for interpreting the behaviour of vehicles on the road. In this Letter, the authors propose a new method that effectively predicts the vehicle behaviour based on structured deep forest modelling. Inspired by structured learning, the structure information of vehicle behaviour is extracted from the detected vehicle, and then the corresponding structured label is constructed. Especially, the structured label visually expresses the vehicle behaviour as contrast to the discrete numerical label. With the structured label, a structured deep forest model is proposed to predict the vehicle behaviour. Experimental results illustrate that the proposed method successfully obtains the implication of semantic interpretation of vehicle behaviour by the predicted structured labels, and meanwhile it achieves comparable performance with traditional methods.

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