From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
This is an incremental review paper addressing simulation issues for autonomous vehicle developers.
The paper provides an overview of challenges in autonomous driving simulation, such as perception- and behavior-realism, and identifies trends like data-driven approaches replacing model-based methods.
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.