README.md

# Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

## [Project Page](https://ap229997.github.io/projects/transfuser/) | [Paper](https://arxiv.org/pdf/2104.09224.pdf) | Supplementary | [Video](https://youtu.be/cc05F56vjVI) | [Poster](https://ap229997.github.io/projects/transfuser/assets/poster.pdf) | Blog

<img src="transfuser/assets/teaser.png" height="192" hspace=30> <img src="transfuser/assets/full_arch.png" width="400">

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 our 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=2000 -opengl
```

#### Without Display

Without Docker:
```
SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE=0 ./CarlaUE4.sh --world-port=2000 -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=0 carlasim/carla:0.9.10.1 ./CarlaUE4.sh --world-port=2000 -opengl
```

Docker 19:
```Shell
docker run -it --rm --net=host --gpus '"device=0"' 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).

### Routes and Scenarios

Each route is defined by a sequence of waypoints (and optionally a weather condition) that the agent needs to follow. Each scenario is defined by a trigger transform (location and orientation) and other actors present in that scenario (optional). The [leaderboard repository](https://github.com/carla-simulator/leaderboard/tree/master/data) provides a set of routes and scenarios files. To generate additional routes, spin up a CARLA server and follow the procedure below.

#### Generating routes with intersections
The position of traffic lights is used to localize intersections and (start_wp, end_wp) pairs are sampled in a grid centered at these points.
```Shell
python3 tools/generate_intersection_routes.py --save_file <path_of_generated_routes_file> --town <town_to_be_used>
```

#### Sampling individual junctions from a route
Each route in the provided routes file is interpolated into a dense sequence of waypoints and individual junctions are sampled from these based on change in navigational commands.
```Shell
python3 tools/sample_junctions.py --routes_file <xml_file_containing_routes> --save_file <path_of_generated_file>
```

#### Generating Scenarios
Additional scenarios are densely sampled in a grid centered at the locations from the [reference scenarios file](https://github.com/carla-simulator/leaderboard/blob/master/data/all_towns_traffic_scenarios_public.json). More scenario files can be found [here](https://github.com/carla-simulator/scenario_runner/tree/master/srunner/data).
```Shell
python3 tools/generate_scenarios.py --scenarios_file <scenarios_file_to_be_used_as_reference> --save_file <path_of_generated_json_file> --towns <town_to_be_used>
```

## Training
The training code and pretrained models are provided below.
```Shell
mkdir model_ckpt
wget https://s3.eu-central-1.amazonaws.com/avg-projects/transfuser/models.zip -P model_ckpt
unzip model_ckpt/models.zip -d model_ckpt/
rm model_ckpt/models.zip
```

- [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
CUDA_VISIBLE_DEVICES=0 ./leaderboard/scripts/run_evaluation.sh
```

## Acknowledgements
This implementation is based on codebase from several 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)