DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch

Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch.

CARLA-Loc: Synthetic SLAM Dataset with Full-stack Sensor Setup in Challenging Weather and Dynamic Environments

The robustness of SLAM algorithms in challenging environmental conditions is crucial for autonomous driving, but the impact of these conditions are unknown while given the difficulty of arbitrarily changing the relevant environmental parameters of the same environment in the real world. Therefore, we propose CARLA-Loc, a synthetic dataset of challenging and dynamic environments built on CARLA simulator. We integrate multiple sensors into the dataset with strict calibration, synchronization and precise timestamping. 7 maps and 42 sequences are posed in our dataset with different dynamic levels and weather conditions. Objects in both stereo images and point clouds are well-segmented with their class labels. We evaluate 5 visual-based and 4 LiDAR-based approaches on varies sequences and analyze the effect of challenging environmental factors on the localization accuracy, showing the applicability of proposed dataset for validating SLAM algorithms.