Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems
In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Future Transportation |
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| Online Access: | https://www.mdpi.com/2673-7590/4/4/74 |
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| author | Mohammed Irshadh Ismaaeel Sathyamangalam Imran Satyesh Shanker Awasthi Michael Khayyat Stefano Arrigoni Francesco Braghin |
| author_facet | Mohammed Irshadh Ismaaeel Sathyamangalam Imran Satyesh Shanker Awasthi Michael Khayyat Stefano Arrigoni Francesco Braghin |
| author_sort | Mohammed Irshadh Ismaaeel Sathyamangalam Imran |
| collection | DOAJ |
| description | In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers. |
| format | Article |
| id | doaj-art-2b14acdb1aca4e4bba064d5cd0a930ba |
| institution | DOAJ |
| issn | 2673-7590 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj-art-2b14acdb1aca4e4bba064d5cd0a930ba2025-08-20T02:55:38ZengMDPI AGFuture Transportation2673-75902024-12-01441537155810.3390/futuretransp4040074Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving SystemsMohammed Irshadh Ismaaeel Sathyamangalam Imran0Satyesh Shanker Awasthi1Michael Khayyat2Stefano Arrigoni3Francesco Braghin4Department of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa, 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa, 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa, 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa, 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via Giuseppe La Masa, 1, 20156 Milano, ItalyIn the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers.https://www.mdpi.com/2673-7590/4/4/74autonomous vehiclemodel predictive controloptimal control problemcontrol barrier functionsobstacle avoidanceuncertainty analysis |
| spellingShingle | Mohammed Irshadh Ismaaeel Sathyamangalam Imran Satyesh Shanker Awasthi Michael Khayyat Stefano Arrigoni Francesco Braghin Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems Future Transportation autonomous vehicle model predictive control optimal control problem control barrier functions obstacle avoidance uncertainty analysis |
| title | Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems |
| title_full | Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems |
| title_fullStr | Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems |
| title_full_unstemmed | Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems |
| title_short | Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems |
| title_sort | virtual validation and uncertainty quantification of an adaptive model predictive controller based motion planner for autonomous driving systems |
| topic | autonomous vehicle model predictive control optimal control problem control barrier functions obstacle avoidance uncertainty analysis |
| url | https://www.mdpi.com/2673-7590/4/4/74 |
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