nuScenes
A multimodal autonomous-driving dataset with synchronized cameras, lidar, radar, maps, and annotations.
About
nuScenes is a strong reference dataset for multimodal autonomy work because its sensor streams can be understood in a shared spatial and temporal context. Camera images, lidar, radar, object annotations, ego pose, and maps all contribute a different part of the scene.
The embedded recording is useful for checking coordinate conventions and understanding what a model input actually represents at one moment in time. From there, cohort-based evaluation can expose where a perception system struggles.
Suggested agent workflows
Copy a prompt into your agent of choice. Each workflow starts small, respects the source terms, and makes the result inspectable in Rerun.
Audit a training slice
Fetch nuScenes from its official source at https://www.nuscenes.org/nuscenes. Read and preserve its source terms and citation requirements. Build a reproducible manifest for a representative slice, then load it into Rerun. Inspect the Camera, Lidar, Radar, Maps streams for timestamp gaps, missing samples, calibration problems, and annotation inconsistencies. Summarize the findings and save both the validation script and a Rerun recording that makes every issue easy to review.
Build a baseline pipeline
Use nuScenes to scaffold a baseline pipeline for 3D detection, Tracking, Prediction. Start with a small deterministic cohort and document the selection criteria. Create the dataset adapter, preprocessing steps, train and validation splits, and one lightweight baseline. Log inputs, labels, predictions, losses, and representative failures to Rerun so I can inspect the full experiment timeline. Include exact setup and reproduction commands.
Find useful failure cohorts
Analyze nuScenes and propose a practical cohort taxonomy for 3D detection, Tracking, Prediction. Use available metadata and the Camera, Lidar, Radar, Maps signals to identify hard cases, rare conditions, coverage gaps, and possible leakage between splits. Materialize the cohorts as queryable metadata, log examples from each cohort to Rerun, and produce a short report explaining which cohorts should become regression tests. Do not infer labels that are not present in the source data.