ChatSim

Editable Scene Simulation for Autonomous Driving via LLM-Agent Collaboration

Arxiv | Project Page | Video

Requirement

• Ubuntu version >= 20.04 (for using Blender 3.+)
• COLMAP or Metashape software (not necessary, we provide recalibrated poses)
• OpenAI API Key

Installation

First clone this repo recursively.

git clone https://github.com/yifanlu0227/ChatSim.git --recursive

Step 1: Setup environment

conda create -n chatsim python=3.8
conda activate chatsim

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
imageio_download_bin freeimage

Step 2: Install McNeRF

The installation is the same as F2-NeRF. Please go through the following steps.

cd chatsim/background/mcnerf/

# mcnerf use the same data directory. 
ln -s ../../../data .

Step 2.1: Install dependencies

For Debian based Linux distributions:

sudo apt install zlib1g-dev

For Arch based Linux distributions:

sudo pacman -S zlib

Step 2.2: Download pre-compiled LibTorch

Taking torch-1.13.1+cu117 for example.

cd chatsim/background/mcnerf
cd External

wget https://download.pytorch.org/libtorch/cu117/libtorch-cxx11-abi-shared-with-deps-1.13.1%2Bcu117.zip
unzip ./libtorch-cxx11-abi-shared-with-deps-1.13.1+cu117.zip
rm ./libtorch-cxx11-abi-shared-with-deps-1.13.1+cu117.zip

Step 2.3: Compile

The lowest g++ version is 7.5.0.

cd ..
cmake . -B build
cmake --build build --target main --config RelWithDebInfo -j

Step 3: Install Inpainting tools

Step 3.1: Setup Video Inpainting

cd ../inpainting/Inpaint-Anything/
python -m pip install -e segment_anything

Go here to download pretrained_models, put the directory into ./ and get ./pretrained_models. Additionally, download pretrain, put the directory into ./pytracking as ./pytracking/pretrain.

Step 3.2: Setup Image Inpainting

cd ../latent-diffusion
pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip
pip install -e .

# download pretrained ldm
wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1

Step 4: Install Blender Software and our Blender Utils

We tested with Blender 3.5.1. Note that Blender 3+ requires Ubuntu version >= 20.04.

Step 4.1: Install Blender software

cd ../../Blender
wget https://download.blender.org/release/Blender3.5/blender-3.5.1-linux-x64.tar.xz
tar -xvf blender-3.5.1-linux-x64.tar.xz
rm blender-3.5.1-linux-x64.tar.xz

Step 4.2: Install blender utils for Blender's python

locate the internal Python of Blender, for example blender-3.5.1-linux-x64/3.5/python/bin/python3.10

export blender_py=$PWD/blender-3.5.1-linux-x64/3.5/python/bin/python3.10

cd utils

# install dependency (use the -i https://pypi.tuna.tsinghua.edu.cn/simple if you are in the Chinese mainland)
$blender_py -m pip install -r requirements.txt 
$blender_py -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

$blender_py setup.py develop

Step 5: Setup Trajectory Tracking Module (optional)

If you want to get smoother and more realistic trajectories, you can install the trajectory module and change the parameter motion_agent-motion_tracking to True in .yaml file. For installation (both code and pretrained model), you can run the following commands in terminal

pip install frozendict gym==0.26.2 stable-baselines3[extra] protobuf==3.20.1

cd chatsim/foreground
git clone --recursive [email protected]:MARMOTatZJU/drl-based-trajectory-tracking.git -b v1.0.0

cd drl-based-trajectory-tracking
source setup-minimum.sh

Then when the parameter motion_agent-motion_tracking is set as True, each trajectory will be tracked by this module to make it smoother and more realistic.

Step 6: Install McLight (optional)

If you want to train the skydome model, follow the READMD in chatsim/foreground/mclight/skydome_lighting/readme.md. You can download our provided skydome HDRI in the next section and start the simulation.

Usage

Data Preparation

Download and extract waymo data

mkdir data
mkdir data/waymo_tfrecords
mkdir data/waymo_tfrecords/1.4.2

Download the waymo perception dataset v1.4.2 to the data/waymo_tfrecords/1.4.2. In the google cloud console, a correct folder path is waymo_open_dataset_v_1_4_2/individual_files/training or waymo_open_dataset_v_1_4_2/individual_files/validation. Static scenes we have used are listed here

Static waymo scenes in training set

• segment-10676267326664322837_311_180_331_180_with_camera_labels
• segment-11379226583756500423_6230_810_6250_810_with_camera_labels
• segment-1172406780360799916_1660_000_1680_000_with_camera_labels
• segment-12879640240483815315_5852_605_5872_605_with_camera_labels
• segment-13085453465864374565_2040_000_2060_000_with_camera_labels
• segment-13142190313715360621_3888_090_3908_090_with_camera_labels
• segment-13196796799137805454_3036_940_3056_940_with_camera_labels
• segment-13238419657658219864_4630_850_4650_850_with_camera_labels
• segment-13469905891836363794_4429_660_4449_660_with_camera_labels
• segment-14004546003548947884_2331_861_2351_861_with_camera_labels
• segment-14333744981238305769_5658_260_5678_260_with_camera_labels
• segment-14348136031422182645_3360_000_3380_000_with_camera_labels
• segment-14424804287031718399_1281_030_1301_030_with_camera_labels
• segment-14869732972903148657_2420_000_2440_000_with_camera_labels
• segment-15221704733958986648_1400_000_1420_000_with_camera_labels
• segment-15270638100874320175_2720_000_2740_000_with_camera_labels
• segment-15349503153813328111_2160_000_2180_000_with_camera_labels
• segment-15365821471737026848_1160_000_1180_000_with_camera_labels
• segment-15868625208244306149_4340_000_4360_000_with_camera_labels
• segment-16345319168590318167_1420_000_1440_000_with_camera_labels
• segment-16470190748368943792_4369_490_4389_490_with_camera_labels
• segment-16608525782988721413_100_000_120_000_with_camera_labels
• segment-16646360389507147817_3320_000_3340_000_with_camera_labels
• segment-17761959194352517553_5448_420_5468_420_with_camera_labels
• segment-3425716115468765803_977_756_997_756_with_camera_labels
• segment-3988957004231180266_5566_500_5586_500_with_camera_labels
• segment-4058410353286511411_3980_000_4000_000_with_camera_labels
• segment-8811210064692949185_3066_770_3086_770_with_camera_labels
• segment-9385013624094020582_2547_650_2567_650_with_camera_labels

Static waymo scenes in validation set

• segment-10061305430875486848_1080_000_1100_000_with_camera_labels
• segment-10247954040621004675_2180_000_2200_000_with_camera_labels
• segment-10275144660749673822_5755_561_5775_561_with_camera_labels

After downloading tfrecords, you should see folder structure like

data
|-- ...
|-- ...
`-- waymo_tfrecords
    `-- 1.4.2
        |-- segment-10247954040621004675_2180_000_2200_000_with_camera_labels.tfrecord
        |-- segment-11379226583756500423_6230_810_6250_810_with_camera_labels.tfrecord
        |-- ...
        `-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels.tfrecord

We extract the images, camera poses, LiDAR file, etc. out of the tfrecord files with the data_utils/process_waymo_script.py.

cd data_utils
python process_waymo_script.py --waymo_data_dir=../data/waymo_tfrecords/1.4.2 --nerf_data_dir=../data/waymo_multi_view

This will generate the data folder data/waymo_multi_view.

Recalibrate waymo data (or just download our recalibrated files)

You can download our recalibration files from here. After everything is done, you should see

If you want to do the recalibration yourself, you need to use COLMAP or Metashape to calibrate images in the data/waymo_multi_view/{SCENE_NAME}/images folder and convert them back to the waymo world coordinate. Please follow the tutorial in data_utils/README.md. And the final camera extrinsics and intrinsics are stored as cam_meta.npy.

data
`-- waymo_multi_view
    |-- ...
    `-- segment-1172406780360799916_1660_000_1680_000_with_camera_labels
        |-- 3d_boxes.npy                # 3d bounding boxes of the first frame
        |-- images                      # a clip of waymo images used in chatsim (typically 40 frames)
        |-- images_all                  # full waymo images (typically 198 frames)
        |-- map.pkl                     # map data of this scene
        |-- point_cloud                 # point cloud file of the first frame
        |-- cams_meta.npy               # Camera ext&int calibrated by metashape and transformed to waymo coordinate system.
        |-- cams_meta_metashape.npy     # Camera ext&int calibrated by metashape (intermediate file, relative scale, not required by simulation inference)
        |-- cams_meta_colmap.npy        # Camera ext&int calibrated by colmap (intermediate file, relative scale, not required by simulation inference)
        |-- cams_meta_waymo.npy         # Camera ext&int from original waymo dataset (intermediate file, not required by simulation inference)
        |-- shutters                    # normalized exposure time (mean=0 std=1)
        |-- tracking_info.pkl           # tracking data
        |-- vehi2veh0.npy               # transformation matrix from i-th frame's vehicle coordinate to the first frame's vehicle 
        |-- sparse/0                    # calibration files from COLMAP (intermediate file, not required by simulation inference)
        `-- camera.xml                  # calibration file from Metashape (intermediate file, not required by simulation inference)
        coordinate.

Coordinate Convention

• Points in point_cloud/000_xxx.pcd are in ego vehicle's coordinate
• Camera poses in camera.xml are RDF convention (x-right, y-down, z-front).
• Camera poses in cams_meta.npy are in RUB convention (x-right, y-up, z-back).
• vehi2veh0.npy transformation between vehicle coordinate, vehicle coordinates are FLU convention (x-front, y-left, z-up), as waymo paper illustrated.

cams_meta.npy instruction

cams_meta.shape = (N, 27)
cams_meta[:, 0 :12]: flatten camera poses in RUB
cams_meta[:, 12:21]: flatten camse intrinsics
cams_meta[:, 21:25]: distortion params [k1, k2, p1, p2]
cams_meta[:, 25:27]: bounds [z_near, z_far] (not used.)

Train the model

Download pretrain for quick start-up

You need to train the McNeRF model for each scene as well as the McLight's skydome estimation network. To get started quickly, you can download our skydome estimation and some Blender 3D assets.

• Skydome HDRI. Download and put them in data/waymo_skydome
• Blender Assets. Download and put them in data/blender_assets. Our 3D models are collected from the Internet. We tried our best to contact the author of the model and ensure that copyright issues are properly dealt with (our open source projects are not for profit). If you are the author of a model and our behavior infringes your copyright, please contact us immediately and we will delete the model.

Train McNeRF

cd chatsim/background/mcnerf

Make sure you have the data folder linking to ../../../data, and train your model with

python scripts/run.py --config-name=wanjinyou_big \
dataset_name=waymo_multi_view case_name=${CASE_NAME} \
exp_name=${EXP_NAME} dataset.shutter_coefficient=0.15 mode=train_hdr_shutter +work_dir=$(pwd) 

where ${CASE_NAME} are those like segment-11379226583756500423_6230_810_6250_810_with_camera_labels and ${EXP_NAME} can be anything like exp_coeff_0.15. dataset.shutter_coefficient = 0.15 or dataset.shutter_coefficient = 0.3 work well.

You can simply run scripts like bash train-1137.sh for training and bash render_novel_view-1137.sh for testing.

Start simulation

export OPENAI_API_KEY=<your api key>

Now you can start the simulation with

python main.py -y ${CONFIG YAML} \
               -p ${PROMPT} \
               -s ${SIMULATION NAME}

• ${CONFIG YAML} specifies the scene information, and yamls are stored in config folder. e.g. config/waymo-1137.yaml.

• ${PROMPT} is your input prompt, which should be wrapped in quotation marks. e.g. add a straight driving car in the scene.

• ${SIMULATION NAME} determines the name of the folder when saving results. default demo.

You can try

python main.py -y config/waymo-1137.yaml -p 'add a straight driving car in the scene' -s demo

The rendered results are saved in results/1137_demo_%Y_%m_%d_%H_%M_%S. Intermediate files are saved in results/cache/1137_demo_%Y_%m_%d_%H_%M_%S for debug and visualization if save_cache are enabled in config/waymo-1137.yaml.

Config file explanation

config/waymo-1137.yaml contains the detailed explanation for each entry. We will give some extra explanation. Suppose the yaml is read into config_dict:

• config_dict['scene']['is_wide_angle'] determines the rendering view. If set to True, we will expand waymo's intrinsics (width -> 3 x width) to render wide-angle images. Also note that is_wide_angle = True comes with rendering_mode = 'render_wide_angle_hdr_shutter'; is_wide_angle = False comes with rendering_mode = 'render_hdr_shutter'

• config_dict['scene']['frames'] the frame number for rendering.

• config_dict['agents']['background_rendering_agent']['nerf_quiet_render'] will determine whether to print the output of mcnerf to the terminal. Set to False for debug use.

• config_dict['agents']['foreground_rendering_agent']['use_surrounding_lighting'] defines whether we use the surrounding lighting. Currently use_surrounding_lighting = True only takes effect when merely one vehicle is added, because HDRI is a global illumination in Blender. It is difficult to set a separate HDRI for each car. use_surrounding_lighting = True can also lead to slow rendering, since it will call nerf #frame times. We set it to False in each default yaml.

• config_dict['agents']['foreground_rendering_agent']['skydome_hdri_idx'] is the filename (w.o. extension) we choose from data/waymo_skydome/${SCENE_NAME}/. It is the skydome HDRI estimation from the first frame('000') by default, but you can manually select a better estimation from another frame. To view the HDRI, we recommend the VERIV for vscode and tev for desktop environment.

Train McLight's Skydome estimation network

Go to chatsim/foreground/mclight/skydome_lighting and follow chatsim/foreground/mclight/skydome_lighting/readme.md for the training.

Todo

• arxiv paper release
• code and model release
• motion tracking module drl-based-trajectory-tracking (to smooth trajectory)
• multi-round wrapper code

Citation

@InProceedings{wei2024editable,
      title={Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents}, 
      author={Yuxi Wei and Zi Wang and Yifan Lu and Chenxin Xu and Changxing Liu and Hao Zhao and Siheng Chen and Yanfeng Wang},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      month={June},
      year={2024},
}