Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions
Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/7496017 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850215603917291520 |
|---|---|
| author | Olivér Törő Tamás Bécsi Szilárd Aradi Péter Gáspár |
| author_facet | Olivér Törő Tamás Bécsi Szilárd Aradi Péter Gáspár |
| author_sort | Olivér Törő |
| collection | DOAJ |
| description | Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the suitability of different probabilistic state estimation methods, namely, the Extended Kalman Filter (EKF) and the more general Particle Filter (PF) with the addition of the Interacting Multiple Model (IMM) approach. These algorithms are not capable of predicting motion for long term in road traffic conditions, though their robustness and model classification capability are essential for the overall system. The performance is evaluated in road traffic scenarios where the tracked object imitates the motion characteristics of a road vehicle and is observed from a stationary sensor. The measurements are generated according to standard automotive radar models. The analysis conducted along two aspects emphasizes the different performance and scaling properties of the examined state estimation algorithms. The presented evaluation framework serves as a customizable method to test and develop advanced autonomous functions. |
| format | Article |
| id | doaj-art-7ee80eb0e69b4cbdbab7866be0c7fd1f |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7ee80eb0e69b4cbdbab7866be0c7fd1f2025-08-20T02:08:35ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/74960177496017Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle FunctionsOlivér Törő0Tamás Bécsi1Szilárd Aradi2Péter Gáspár3Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u.2., H-1111 Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u.2., H-1111 Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u.2., H-1111 Budapest, HungaryComputer and Automation Research Institute, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest, HungaryRobust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the suitability of different probabilistic state estimation methods, namely, the Extended Kalman Filter (EKF) and the more general Particle Filter (PF) with the addition of the Interacting Multiple Model (IMM) approach. These algorithms are not capable of predicting motion for long term in road traffic conditions, though their robustness and model classification capability are essential for the overall system. The performance is evaluated in road traffic scenarios where the tracked object imitates the motion characteristics of a road vehicle and is observed from a stationary sensor. The measurements are generated according to standard automotive radar models. The analysis conducted along two aspects emphasizes the different performance and scaling properties of the examined state estimation algorithms. The presented evaluation framework serves as a customizable method to test and develop advanced autonomous functions.http://dx.doi.org/10.1155/2019/7496017 |
| spellingShingle | Olivér Törő Tamás Bécsi Szilárd Aradi Péter Gáspár Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions Journal of Advanced Transportation |
| title | Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions |
| title_full | Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions |
| title_fullStr | Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions |
| title_full_unstemmed | Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions |
| title_short | Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions |
| title_sort | sensitivity and performance evaluation of multiple model state estimation algorithms for autonomous vehicle functions |
| url | http://dx.doi.org/10.1155/2019/7496017 |
| work_keys_str_mv | AT olivertoro sensitivityandperformanceevaluationofmultiplemodelstateestimationalgorithmsforautonomousvehiclefunctions AT tamasbecsi sensitivityandperformanceevaluationofmultiplemodelstateestimationalgorithmsforautonomousvehiclefunctions AT szilardaradi sensitivityandperformanceevaluationofmultiplemodelstateestimationalgorithmsforautonomousvehiclefunctions AT petergaspar sensitivityandperformanceevaluationofmultiplemodelstateestimationalgorithmsforautonomousvehiclefunctions |