OpenDriveLab/UniAD

Health v2 · maintenance only

Adoption not part of the score

4737 stars · 549 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

UniAD is a unified end-to-end autonomous driving framework that hierarchically casts perception, prediction, and planning tasks under a planning-oriented philosophy. It won the CVPR 2023 Best Paper Award and was released as UniAD 2.0 in October 2025 with upgrades to mmdet3d 1.x and PyTorch 2.x.

Use cases

• run end-to-end autonomous driving research on nuScenes
• benchmark motion prediction for autonomous vehicles
• evaluate open-loop planning with collision metrics
• train multi-task perception-prediction-planning models
• do BEV segmentation and occupancy prediction from camera data
• reproduce CVPR 2023 best paper results

When to choose

• you need a unified multi-task autonomous driving pipeline rather than standalone modules
• you want a research baseline for end-to-end planning on nuScenes
• you need SOTA motion prediction and occupancy prediction benchmarks

When to avoid

• you need a production-ready autonomous driving stack for real vehicles
• you lack the required GPUs or nuScenes dataset access
• you only need a single task like object detection without the full pipeline

Facets

framework · maturity active

machine-learning deep-learning computer-vision simulation autonomous-vehicles deep-learning computer-vision artificial-intelligence python autonomous-driving end-to-end motion-prediction multi-object-tracking planning bev-segmentation cvpr-2023 nuscenes nuplan gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenDriveLab/UniAD")