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Performance of Sensor Fusion for Vehicular Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Performance of Sensor Fusion for Vehicular Applications./
作者:	Janevski, Nikola.
面頁冊數:	1 online resource (137 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
標題:	Global positioning systems--GPS. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29283462click for full text (PQDT)
ISBN:	9798845456175

Performance of Sensor Fusion for Vehicular Applications.
Janevski, Nikola.

Performance of Sensor Fusion for Vehicular Applications. - 1 online resource (137 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--West Virginia University, 2022.

Includes bibliographical references

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The performance of the sensor fusion depends on many factors such as the sensors used, the kinematic model used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type of road, the density of vehicles, and the gating methods. The interactions between parameters and the extent to which individual parameters contribute to the overall accuracy of a sensor fusion system can be difficult to assess.In this study, a full-factorial experimental evaluation of a sensor fusion system based on a real vehicle was performed. The experimental results for different driving scenarios and parameters are discussed and the factors that make the most impact are identified. The performance of sensor fusion depends on many factors such as the sensors used, the kinematic model used in the Extended Kalman Filter (EKF) motion of the vehicles, type of road, density of vehicles, and gating methods.This study identified that the distance between the vehicles has the largest impact on the estimation error because the vision sensor performs poorly with increased distance.In addition, it was identified that the kinematic models had no significant impact on the estimation. Last but not least, the ellipsoid gates performed better than rectangular gates. In addition, we propose a new gating algorithm called an angular gate. This algorithm is based on the observation that the data for each target lies in the direction of that target. Therefore, the angle and the range can be used for setting up a two-level gating approach that is both more intuitive and computationally faster than ellipsoid gates. The angular gates can achieve a speedup factor of up to 2.27 compared to ellipsoid gates.Furthermore, we provide time complexity analysis of angular gates, ellipsoid gates, and rectangular gates demonstrating the theoretical reasons why angular gates perform better.Last, we evaluated the performance of the Munkres algorithm using a full factorial design and identified that narrower gates can speedup the running time of the Munkres algorithm and, surprisingly, even improve the RMSE in some cases.The low target maneuvering index of vehicular systems was identified as the reason why the kinematic models do not have an impact on the estimation. This finding supports the use of simpler and computationally inexpensive filters instead of complex Interacting Multiple Model filters. The angular gates also improve the computational efficiency of the overall sensor fusion system making them suitable for vehicular application as well as for embedded systems and robotics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845456175Subjects--Topical Terms:

3559357
Global positioning systems--GPS.
Index Terms--Genre/Form:

542853
Electronic books.

Performance of Sensor Fusion for Vehicular Applications.
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245 1 0 $a Performance of Sensor Fusion for Vehicular Applications.
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520 $a Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The performance of the sensor fusion depends on many factors such as the sensors used, the kinematic model used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type of road, the density of vehicles, and the gating methods. The interactions between parameters and the extent to which individual parameters contribute to the overall accuracy of a sensor fusion system can be difficult to assess.In this study, a full-factorial experimental evaluation of a sensor fusion system based on a real vehicle was performed. The experimental results for different driving scenarios and parameters are discussed and the factors that make the most impact are identified. The performance of sensor fusion depends on many factors such as the sensors used, the kinematic model used in the Extended Kalman Filter (EKF) motion of the vehicles, type of road, density of vehicles, and gating methods.This study identified that the distance between the vehicles has the largest impact on the estimation error because the vision sensor performs poorly with increased distance.In addition, it was identified that the kinematic models had no significant impact on the estimation. Last but not least, the ellipsoid gates performed better than rectangular gates. In addition, we propose a new gating algorithm called an angular gate. This algorithm is based on the observation that the data for each target lies in the direction of that target. Therefore, the angle and the range can be used for setting up a two-level gating approach that is both more intuitive and computationally faster than ellipsoid gates. The angular gates can achieve a speedup factor of up to 2.27 compared to ellipsoid gates.Furthermore, we provide time complexity analysis of angular gates, ellipsoid gates, and rectangular gates demonstrating the theoretical reasons why angular gates perform better.Last, we evaluated the performance of the Munkres algorithm using a full factorial design and identified that narrower gates can speedup the running time of the Munkres algorithm and, surprisingly, even improve the RMSE in some cases.The low target maneuvering index of vehicular systems was identified as the reason why the kinematic models do not have an impact on the estimation. This finding supports the use of simpler and computationally inexpensive filters instead of complex Interacting Multiple Model filters. The angular gates also improve the computational efficiency of the overall sensor fusion system making them suitable for vehicular application as well as for embedded systems and robotics.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
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650 4 $a Computer science. $3 523869
650 4 $a Autonomous vehicles. $3 2179092
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650 4 $a Engineering. $3 586835
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650 4 $a Physics. $3 516296
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