Driver lane change intention inference using machine learning methods.
| dc.contributor.advisor | Cao, Dongpu | |
| dc.contributor.advisor | Velenis, Efstathios | |
| dc.contributor.author | Xing, Yang | |
| dc.date.accessioned | 2023-05-25T09:04:40Z | |
| dc.date.available | 2023-05-25T09:04:40Z | |
| dc.date.issued | 2018-04 | |
| dc.description.abstract | Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour. | en_UK |
| dc.description.coursename | PhD in Transport | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19707 | |
| dc.language.iso | en | en_UK |
| dc.rights | © Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | Driver intention inference | en_UK |
| dc.subject | machine learning | en_UK |
| dc.subject | ADAS | en_UK |
| dc.subject | driver behaviours | en_UK |
| dc.subject | machine vision | en_UK |
| dc.title | Driver lane change intention inference using machine learning methods. | en_UK |
| dc.type | Thesis | en_UK |