Add dataset card and metadata

Hi, I'm Niels, part of the community science team at Hugging Face.

This PR aims to improve the dataset card for Fail2Drive by:
- Adding the `robotics` task category to the metadata.
- Including links to the paper, project page, and GitHub repository.
- Providing a description of the benchmark's goals and components.
- Adding installation and sample usage instructions directly from the official repository to help users get started.

README.md

@@ -0,0 +1,70 @@
+---
+task_categories:
+- robotics
+---
+
+# Fail2Drive: Benchmarking Closed-Loop Driving Generalization
+
+[**Project Page**](https://simonger.github.io/fail2drive/) | [**Paper**](https://huggingface.co/papers/2604.08535) | [**GitHub**](https://github.com/autonomousvision/fail2drive)
+
+Fail2Drive is the first CARLA v2 benchmark designed to test closed-loop generalization on truly unseen long-tail scenarios. By pairing each shifted route with an in-distribution reference scenario, it exposes substantial hidden failure modes in current state-of-the-art driving models.
+
+## Highlights
+- **17 unseen scenarios** for evaluation of true generalization.
+- **30 novel assets** including animals, visual noise, and adversarial obstacles.
+- **Paired route design** enables quantification of the generalization gap.
+- **100 route pairs** in diverse environments and configurations.
+- **Toolbox** for creating custom obstacles and routes.
+
+## Installation
+
+To set up the environment and the Fail2Drive CARLA simulator, follow these steps:
+
+```bash
+# 1. Clone this repository
+git clone https://github.com/autonomousvision/fail2drive.git
+cd fail2drive
+
+# 2. Set up the Fail2Drive CARLA simulator
+mkdir f2d_carla
+curl -L \
+  https://huggingface.co/datasets/SimonGer/fail2drive/resolve/main/fail2drive_simulator.tar.gz \
+  | tar -xz -C f2d_carla
+
+# 3. Create the conda environment
+conda env create -f environment.yml
+conda activate fail2drive
+
+# 4. Set environment variables
+source env_vars.sh
+```
+
+## Sample Usage
+
+To run a keyboard-controlled human agent on a benchmark route, start CARLA in a separate terminal and run:
+
+```bash
+python leaderboard/leaderboard/leaderboard_evaluator.py \
+  --agent ${WORK_DIR}/leaderboard/leaderboard/autoagents/human_agent_keyboard.py \
+  --routes ${WORK_DIR}/fail2drive_split/Generalization_PedestriansOnRoad_1085.xml
+```
+
+To run the PDM-Lite expert policy:
+
+```bash
+python leaderboard/leaderboard/leaderboard_evaluator_local.py \
+  --agent ${WORK_DIR}/team_code/visu_agent.py \
+  --track MAP \
+  --routes ${WORK_DIR}/fail2drive_split/Generalization_PedestriansOnRoad_1085.xml
+```
+
+## Citation
+
+```bibtex
+@article{gerstner2024fail2drive,
+  title={Fail2Drive: Benchmarking Closed-Loop Driving Generalization},
+  author={Gerstner, Simon and others},
+  journal={arXiv preprint arXiv:2604.08535},
+  year={2024}
+}
+```