Update README.md

README.md

 # TransFuser
-Official code release for CVPR 2021 paper "Multi-Modal Fusion Transformer for End-to-End Autonomous Driving"
+
+![TransFuser](assets/transfuser.png)
+
+This repository contains the code for the CVPR 2021 paper [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](http://www.cvlibs.net/publications/Prakash2021CVPR.pdf). If you find out code or paper useful, please cite
+```bibtex
+@inproceedings{Prakash2021CVPR,
+  author = {Prakash, Aditya and Chitta, Kashyap and Geiger, Andreas},
+  title = {Multi-Modal Fusion Transformer for End-to-End Autonomous Driving},
+  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year = {2021}
+}
+```
+
+## Setup
+Install anaconda
+```Shell
+wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
+bash Anaconda3-2020.11-Linux-x86_64.sh
+source ~/.profile
+```
+
+Clone the repo and build the environment
+
+```Shell
+git clone https://github.com/autonomousvision/transfuser
+cd transfuser
+conda create -n transfuser python=3.7
+pip3 install -r requirements.txt
+conda activate transfuser
+```
+
+Download and setup CARLA 0.9.10.1
+```Shell
+chmod +x setup_carla.sh
+./setup_carla.sh
+```
+
+## Data Generation
+The training data is generated using ```leaderboard/team_code/auto_pilot.py``` in 8 CARLA towns and 14 weather conditions. The routes and scenarios files to be used for data generation are provided at ```leaderboard/data```.
+
+### Running CARLA Server
+
+#### With Display
+```Shell
+./CarlaUE4.sh -world-port=<port> -opengl
+```
+
+#### Without Display
+
+Without Docker:
+```
+SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE=<gpu_id> ./CarlaUE4.sh -world-port=<port> -opengl
+```
+
+With Docker:
+
+Instructions for setting up docker are available [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker). Pull the docker image of CARLA 0.9.10.1 ```docker pull carlasim/carla:0.9.10.1```.
+
+Docker 18:
+```
+docker run -it --rm -p 2000-2002:2000-2002 --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=<gpu_id> carlasim/carla:0.9.10.1 ./CarlaUE4.sh -world-port=2000 -opengl
+```
+
+Docker 19:
+```Shell
+docker run -it --rm --net=host --gpus '"device=<gpu_id>"' carlasim/carla:0.9.10.1 ./CarlaUE4.sh -world-port=2000 -opengl
+```
+
+If the docker container doesn't start properly then add another environment variable ```-e SDL_AUDIODRIVER=dsp```.
+
+### Run the Autopilot
+
+Once the CARLA server is running, rollout the autopilot to start data generation.
+```Shell
+./leaderboard/scripts/run_evaluation.sh
+```
+The expert agent used for data generation is defined in ```leaderboard/team_code/auto_pilot.py```. Different variables which need to be set are specified in ```leaderboard/scripts/run_evaluation.sh```. The expert agent is based on the autopilot from [this codebase](https://github.com/bradyz/2020_CARLA_challenge).
+
+## Training
+The training code and pretrained models for different models used in our paper are provided below.
+- [CILRS](cilrs)
+- [LBC](https://github.com/bradyz/2020_CARLA_challenge)
+- [AIM](aim)
+- [Late Fusion](late_fusion)
+- [Geometric Fusion](geometric_fusion)
+- [TransFuser](transfuser)
+
+## Evaluation
+Spin up a CARLA server (described above) and run the required agent. The adequate routes and scenarios files are provided in ```leaderboard/data``` and the required variables need to be set in ```leaderboard/scripts/run_evaluation.sh```.
+```Shell
+./leaderboard/scripts/run_evaluation.sh
+```
+
+## Acknowledgements
+This implementation uses code from several amazing repositories.
+- [2020_CARLA_challenge](https://github.com/bradyz/2020_CARLA_challenge)
+- [OATomobile](https://github.com/OATML/oatomobile)
+- [CARLA Leaderboard](https://github.com/carla-simulator/leaderboard)
+- [Scenario Runner](https://github.com/carla-simulator/scenario_runner)