國立虎尾科技大學 |

Progress Towards LiDAR Based Bicycle Detection in Urban Environments.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Progress Towards LiDAR Based Bicycle Detection in Urban Environments./
作者:	Rufo, Antonio Antonellis.
面頁冊數:	1 online resource (61 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Robotics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355541335

Progress Towards LiDAR Based Bicycle Detection in Urban Environments.
Rufo, Antonio Antonellis.

Progress Towards LiDAR Based Bicycle Detection in Urban Environments. - 1 online resource (61 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Northeastern University, 2017.

Includes bibliographical references

The achievement of level 5 autonomous vehicles on urban roads requires performance equal to that of a human driver in every scenario. In order to achieve this level of autonomy many challenging obstacles must be overcome. In this paper, we will address the specific challenges bicycles pose for self-driving cars in urban environments. One of the most prevalent challenges is detection and tracking of bicycles. Their relatively transparent profile, which changes as the bicycle moves, and their slight frames make detection a difficult problem. Furthermore, their ability to quickly maneuver in cluttered urban environments can generate inaccurate tracking models and faulty prediction estimates. Significant work has been done in sensor and algorithm development to solve the bicycle detection, tracking, and prediction problem, yet problems remain as datasets and algorithm analysis are not accessible to academic researchers. This information is instead considered proprietary. Of the published work in this field, most approaches use idealistic datasets that do not accurately represent real world conditions in order to improve the quality of their results.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355541335Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

Progress Towards LiDAR Based Bicycle Detection in Urban Environments.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Includes supplementary digital materials.
500 $a Adviser: Hanumant Singh.
502 $a Thesis (M.S.)--Northeastern University, 2017.
504 $a Includes bibliographical references
520 $a The achievement of level 5 autonomous vehicles on urban roads requires performance equal to that of a human driver in every scenario. In order to achieve this level of autonomy many challenging obstacles must be overcome. In this paper, we will address the specific challenges bicycles pose for self-driving cars in urban environments. One of the most prevalent challenges is detection and tracking of bicycles. Their relatively transparent profile, which changes as the bicycle moves, and their slight frames make detection a difficult problem. Furthermore, their ability to quickly maneuver in cluttered urban environments can generate inaccurate tracking models and faulty prediction estimates. Significant work has been done in sensor and algorithm development to solve the bicycle detection, tracking, and prediction problem, yet problems remain as datasets and algorithm analysis are not accessible to academic researchers. This information is instead considered proprietary. Of the published work in this field, most approaches use idealistic datasets that do not accurately represent real world conditions in order to improve the quality of their results.
520 $a To further the development of LiDAR sensors and algorithms this thesis introduces the first open LiDAR dataset, collected in real world environments. Algorithms from various papers and publications are combined to create a unique implementation that performs in real world scenarios. The author presents realistic datasets taken with affordable sensors, along with qualitative performance results of leading algorithms. Easy access to this dataset and analysis allows researchers and developers to create systems and algorithms that perform in real world scenarios.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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