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Title: Advanced road and obstacle analysis for intelligent vehicles
Author: Wang, Yifei
ISNI:       0000 0004 2725 4772
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2012
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Road and obstacle analysis are two of the essential building blocks in both Driver Assistance Systems (DAS) and Autonomous Transportation Systems (ATS). Our research focus is to develop computationally efficient algorithm for accurate de- tection of the road boundaries and potential obstacles ba ed on prior knowledge of the highway and urban environments. In this thesis, a novel lane feature extrac- tion algorithm is introduced. It incorporates the global lane shape information to accurately extract feature points that overlap with the lane boundaries. It can be used a a general framework to improve or refine the feature map obtained with a diverse range of local feature extractors. At the lane tracking stage, the performance of the Gaussian Particle Filter (GPF), Gaussian Sum Particle Filter (GSPF) and Sampling Importance Resampling (SIR) particle filter are compared. The GSPF shows a preferable characteristic which is suitable for the lane track- ing application and leads to the best results. For motion-based obstacle detec- tion, we propose a computationally efficient image warping algorithm for motion compensation. This algorithm achieves higher efficiency as well as identical re- sults to perspective mapping based approaches. Furthermore, we investigated stereo vision based obstacle detection and developed a disparity calculation algo- rithm using multi-pass aggregation and local optimisation which utilises the prior knowledge of the traffic scene. This algorithm achieves comparable results to the global optimisation based algorithms with lower computational complexity. Dur- ing the obstacle detection stage, the G-Disparity image. which encloses disparity gradient information, is proposed. Using G-Disparity in conjunction with the -V-Di parity images allows more efficient obstacle extraction with performance improvement over the conventional U- V-Disparity based approaches.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available