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name: transfuser description: "Guides TransFuser autonomous-driving model training, multimodal CARLA sensor-agent operation, dataset and route preparation, Longest6 evaluation, and result analysis." disable-model-invocation: true metadata: disco-role: operating license: MIT

TransFuser

Use this repo skill when a task mentions TransFuser, transformer-based sensor fusion for CARLA, camera/LiDAR imitation learning, HybridAgent, CARLA Longest6, route/scenario generation, or the repository's training/evaluation artifacts.

Choose the route

• Train, resume, validate data, select a fusion backbone, or inspect a checkpoint: read model-training.
• Configure or diagnose the learned CARLA sensor agent, sensors, target point, PID control, safety, or ensembles: read sensor-agent.
• Validate dataset trees, route/scenario XML/JSON, generate routes, or plan privileged data collection: read data-and-routes.
• Prepare local/Longest6 evaluation, parse result JSON, build a guarded evaluation/Docker plan, or interpret infractions: read carla-evaluation.

Read installation-and-compatibility.md first when setting up a new runtime, changing the legacy dependency stack, or checking whether CUDA/CARLA prerequisites are available. Read troubleshooting.md when a workflow fails at installation, import, data/config validation, backend setup, or external runtime boundaries. Read repo-provenance.md before deciding whether this skill is stale for a changed checkout.

Operating contract

1. Establish the checkout, dataset/checkpoint paths, requested backbone, target CARLA version, and whether the task is static preparation, a safe smoke, or full simulation/training.
2. Run the nearest bundled preflight before an expensive action. Bundled helpers are plan/validation tools: they do not download the 210-GB dataset, launch CARLA, train for epochs, build Docker images, or submit cloud jobs.
3. Preserve the repository's coordinate and file-layout conventions. Do not silently reinterpret camera/LiDAR axes, route IDs, DDP checkpoint prefixes, args.txt, or the TEAM_CONFIG directory contract.
4. Treat CUDA as required for learned training and inference. A CPU import or parser check is not a substitute. Treat CARLA 0.9.10.1 plus a running compatible server as required for simulation-native data generation and evaluation.
5. Keep external side effects explicit: ask for or verify network, disk, simulator, GPU, Docker, credentials, and checkpoint prerequisites before launching them.

Installation and minimal checks

The historical repository environment is Python 3.7 with a CUDA 11.3 PyTorch 1.12.1 stack and matching OpenMMLab 1.x packages. Prefer the exact documented combination for compatibility; do not upgrade one compiled component in isolation. A minimal package-only check is:

python -c "import torch; print(torch.__version__, torch.version.cuda, torch.cuda.is_available())"
python scripts/check_environment.py --json

The learned workflows additionally need the repository's top-level modules on PYTHONPATH or an equivalent checkout-aware launch context, compatible mmcv-full, mmdet, mmsegmentation, mmcls, torch-scatter, timm, and the data/vision dependencies. Full sensor-agent and CARLA evaluation also need the CARLA 0.9.10.1 Python API, scenario-runner, leaderboard modules, a running CARLA server, model checkpoints, and route/scenario files.

Scope boundaries

This skill distills the public TransFuser operating workflows; it is not a vendored copy of CARLA or ScenarioRunner. Large maps, figures, downloaded weights, generated results, and the full dataset are not runtime dependencies of this skill. Docker packaging and Alpha leaderboard submission are described as guarded external workflows, not automatically executed.