From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving

• URL: http://arxiv.org/abs/2305.13960v3
• Date: Mon, 31 Jul 2023 11:41:18 GMT
• Title: From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
• Authors: Ferdinand M\"utsch, Helen Gremmelmaier, Nicolas Becker, Daniel Bogdoll, Marc Ren\'e Zofka, J. Marius Z\"ollner
• Abstract summary: We provide an overview of challenges with regard to different aspects and types of simulation. 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.
• Score: 26.397030011439163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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