Berkeley Deep Drive-X (eXplanation) is a dataset is composed of over 77 hours of driving within 6,970 videos. The videos are taken in diverse driving conditions, e.g. day/night, highway/city/countryside, summer/winter etc. On average 40 seconds long, each video contains around 3-4 actions, e.g. speeding up, slowing down, turning right etc., all of which are annotated with a description and an explanation. Our dataset contains over 26K activities in over 8.4M frames.
2022-8-7 arXiv roundup: Adam and sharpness, Recursive self-improvement for coding, Training and model tweaks
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model
Binary decision diagram - Wikipedia
BDD-X Dataset Papers With Code
Towards Knowledge-driven Autonomous Driving
DDI-100 Dataset Papers With Code
PDF] Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems
BDD100K Dataset Papers With Code
Exploring the Berkeley Deep Drive Autonomous Vehicle Dataset, by Jimmy Guerrero, Voxel51
ContrXT: Generating contrastive explanations from any text classifier - ScienceDirect
BDD100K: A Large-scale Diverse Driving Video Database – The Berkeley Artificial Intelligence Research Blog
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