A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
This platform addresses the problem of generating diverse and high-quality synthetic data for training autonomous driving systems, particularly for rare and challenging human-interaction scenarios, which are difficult to obtain from real-world data.
This paper introduces a platform for modeling dynamic and interactive scenarios in autonomous driving, generating these scenarios in simulation with labeled sensor data, and collecting this information for data augmentation. The platform aims to address the challenge of training machine learning modules for autonomous vehicles on rare scenarios by providing synthetic data.
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, such as perception, behavior prediction, and planning. These modules require training datasets with high-quality labels and a diverse range of realistic dynamic behaviors. Consequently, training such modules to handle rare scenarios is difficult because they are, by definition, rarely represented in real-world datasets. Hence, there is a practical need to augment datasets with synthetic data covering these rare scenarios. In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation. To our knowledge, this is the first integrated platform for these tasks specialized to the autonomous driving domain.