NEAT: Neural Attention Fields for End-to-End Autonomous Driving

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This repository is for the ICCV 2021 paper NEAT: Neural Attention Fields for End-to-End Autonomous Driving.

@inproceedings{Chitta2021ICCV,
  author = {Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas},
  title = {NEAT: Neural Attention Fields for End-to-End Autonomous Driving},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
}

Setup

Please follow the installation instructions from our TransFuser repository to set up the CARLA simulator. The conda environment required for NEAT can be installed via:

conda env create -f environment.yml
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

For running the AIM-VA baseline, you will additionally need to install MMCV and MMSegmentation.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
pip install mmsegmentation

Data Generation

The training data is generated using leaderboard/team_code/auto_pilot.py. Data generation requires 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). We provide several routes and scenarios under leaderboard/data/. The TransFuser repository and leaderboard repository provide additional routes and scenario files.

Running a CARLA Server

With Display

./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. 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:

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.

Running the Autopilot

Once the CARLA server is running, rollout the autopilot to start data generation.

./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 originally based on the autopilot from this codebase.

Training

The training code and pretrained models are provided below.

mkdir model_ckpt
wget https://s3.eu-central-1.amazonaws.com/avg-projects/neat/models.zip -P model_ckpt
unzip model_ckpt/models.zip -d model_ckpt/
rm model_ckpt/models.zip

There are 5 pretrained models provided in model_ckpt/:

• AIM-MT (2D): aim_mt_sem and aim_mt_sem_depth
• AIM-MT (BEV): aim_mt_bev
• AIM-VA: aim_va
• NEAT: neat

Additional baselines are available in the TransFuser repository.

Evaluation

Spin up a CARLA server (described above) and run the required agent. The required variables need to be set in leaderboard/scripts/run_evaluation.sh.

CUDA_VISIBLE_DEVICES=0 ./leaderboard/scripts/run_evaluation.sh

CARLA also has an official Autonomous Driving Leaderboard on which different models can be evaluated. The leaderboard_submission branch of the TransFuser repository provides details on how to build a docker image and submit it to the leaderboard.

Acknowledgements

This implementation primarily extends the existing TransFuser repository.

If you found our work interesting, check out these other papers on autonomous driving from our group.

• Prakash et al. - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving (CVPR'21)
• Behl et al. - Label efficient visual abstractions for autonomous driving (IROS'20)
• Ohn-Bar et al. - Learning Situational Driving (CVPR'20)
• Prakash et al. - Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving (CVPR'20)