DBNet

Synchronizes Velodyne LiDAR point clouds, dashboard camera videos, and vehicle sensors (speed, steering angle), enabling comprehensive learning from both visual and depth inputs, as highlighted in the CVPR 2018 paper.

Offers extensive, high-resolution samples that support training robust models, evidenced by its use in organized challenges for major conferences like CVPR/ICCV/ECCV.

Experiments demonstrate that adding LiDAR depth information improves accuracy in predicting driving policies, validating the dataset's design for enhanced learning.

Provides baseline models and standardized evaluation metrics (accuracy, AUC, error measures) in the codebase, facilitating reproducible research and direct comparisons.