Synthetic Driving

Synthetic driving data is revolutionizing autonomous vehicle development by providing large, diverse, and consistently annotated datasets for training perception and prediction models. Current research focuses on generating realistic synthetic data encompassing various challenging conditions (e.g., nighttime, adverse weather) and incorporating rich contextual information like infrastructure details and vehicle-to-everything communication data. This allows researchers to explore advanced model architectures, such as those based on GANs and masked autoencoders, for tasks like depth estimation, trajectory prediction, and keypoint detection, ultimately improving the robustness and safety of autonomous driving systems. The availability of these synthetic datasets and associated benchmarks is accelerating progress in the field by mitigating the limitations of real-world data collection.