Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines

Master thesis (2019)

Authors

R. Yu Mechanical Engineering

Contributors

D. Gavrila Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 1)
A. Tejada Ruiz Delft Center for Systems and Control (supervisor 2)
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (coach)
Faculty
Mechanical Engineering
Copyright: © 2019 Rui Yu
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Published Date

25-09-2019

Language

English

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Faculty

Mechanical Engineering

Abstract

The development of intelligent vehicle and autonomous driving asked a higher requirement of ADAS on its functionality. Currently, ADAS systems are able to detect and segment urban and highway driving scenes. They cannot, in general, extract ’meaning’ from this segmentation yet. Learning the intention of other road users will help ADAS understand surroundings and make a response. In a highway scenario, understanding what the preceding vehicle is about to do, is the minimum level of understanding the environment in order to take a decision about your own actions. Among the driving behaviors the preceding vehicle could do, lane change is a complex and dangerous one. Thus, we aimed to develop a real-time lane change intention recognition model. This report presents three models inspired by the Hidden Markov Models (HMMs) and Relevance Vector Machines (RVMs). Besides these two methods, we proposed a new model which combines them and overcome both of their main shortcomings. According to the testing result, the proposed model can correctly recognize more than 95% of the driving behaviors within 1 second the behavior starts, while the F1 score is also as high as 0.98. Besides the high accuracy, the model also has a good performance on the flexibility, testing complexity and the generalization ability.