Thinklab-SJTU/Bench2Drive resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1926 stars · 142 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Bench2Drive is a closed-loop benchmark and dataset for end-to-end autonomous driving, built on CARLA with an RL-based expert driver (Think2Drive). It provides multi-sensor driving data across 44 scenarios and evaluation tooling for E2E-AD models.

Use cases

• benchmark end-to-end autonomous driving models in closed loop
• download driving datasets for training AD models
• evaluate driving policies on CARLA scenarios
• train imitation learning agents from expert demonstrations
• run ablation studies on a small validation subset
• test robustness of driving models to sensor failures

When to choose

• you need a standardized closed-loop benchmark for end-to-end autonomous driving research
• you want uniformly distributed scenario training data with 3D occupancy labels
• you need an RL expert for demonstration data in CARLA

When to avoid

• you need real-world driving data rather than CARLA simulation
• you work outside Python 3.7/3.8 CARLA constraints without the protocol bridge
• you lack GPU resources for large-scale simulation and training

Facets

dataset · maturity active

benchmarking machine-learning simulation data-generation autonomous-vehicles machine-learning simulation computer-vision python autonomous-driving carla end-to-end-driving closed-loop-benchmark world-model reinforcement-learning-expert neurips-2024 3d-occupancy linux docker

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markdown · JSON · MCP: product_card(name="Thinklab-SJTU/Bench2Drive")