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|Title=From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving | |Title=From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving | ||
|Year=2023 | |Year=2023 | ||
+ | |Journal=IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Vision-Centric Autonomous Driving (VCAD) | ||
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{{Publikation Details | {{Publikation Details |
Version vom 15. Juni 2023, 22:54 Uhr
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
Veröffentlicht: 2023
Journal: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Vision-Centric Autonomous Driving (VCAD)
Nicht-referierte Veröffentlichung
Kurzfassung
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world experiments, various challenges still prevent virtual testing from entirely replacing physical test-drives. Our work provides an overview of these challenges with regard to different aspects and types of simulation and subsumes current trends to overcome them. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.
Download: Media:From Model-Based to Data-Driven Simulation Challenges and Trends in Autonomous Driving - camera-ready.pdf
DOI Link: 10.48550/arXiv.2305.13960
Angewandte Technisch-Kognitive Systeme
Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Vision-Centric Autonomous Driving (VCAD)