xrenaa/safety-aware-motion-prediction

[ICCV2021] Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
Xuanchi Ren, Tao Yang, Li Erran Li, Alexandre Alahi, and Qifeng Chen
ICCV 2021

[Paper] [Supplementary material]

Recent Updates

✅ Update data preprocessing code
✅ Update model
🔲 Training script

Installation

Cloning

1. Clone this repository with the following command:

git clone https://github.com/xrenaa/Safety-Aware-Motion-Prediction.git
cd experiments/nuScenes
git clone https://github.com/nutonomy/nuscenes-devkit.git
git checkout 12fb09169eb8ebf04bc39a30cd50334215769c3e

2. Replace experiments/nuScenes/nuscenes-devkit/python-sdk/nuscenes/prediction/input_representation/static_layers.py with the file Here.

Environment Setup

First, we'll create a conda environment to hold the dependencies.

conda create --name safeDrive python=3.6 -y
source activate safeDrive
pip install -r requirements.txt

Data Setup

nuScenes Dataset

1. Download the nuScenes dataset (this requires signing up on their website). Extract the downloaded zip file's contents and place them in the experiments/nuScenes directory. Then, download the map expansion pack (v1.2) and copy the contents of the extracted maps folder into the experiments/nuScenes/maps folder. Eventually you should have the following folder structure:

experiments/nuscenes
    samples	        -	Sensor data for keyframes.
    sweeps	        -	Sensor data for intermediate frames.
    maps	        -	Folder for all map files: rasterized .png images and vectorized .json files.
    v1.0-trainval	-	JSON tables that include all the meta data and annotations.
    process_data.py	-	Our provided data processing script.

2. Finally, process them into a data format that our model can work with.

cd experiments/nuScenes

# For the tranval nuScenes dataset, use the following
python process_data.py --data ../nuScenes --split train --img_size 128
python process_data.py --data ../nuScenes --split train_val --img_size 128
python process_data.py --data ../nuScenes --split val --img_size 128

We provide a notebook to visualize the processed data.

Citation

@inproceedings{ren2021unseen,
  title   = {Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving},
  author  = {Ren, Xuanchi, and Yang, Tao, and Li, Li Erran, and Alahi, Alexandre, and Chen, Qifeng},
  booktitle = {ICCV},
  year    = {2021}
}

safety-aware-motion-prediction's Issues

Training scripts needed

Dear author, your work is very excellent! I'm very interested at your training scripts to do some experiments. Please release your whole codes.

How about the code

Hi! I've red this paper, and how can I get the code ?

Yraining script required

Hello Ren,

I am working on a project where I can use your code base for the paper "safety aware motion prediction"(iccv2021). unfortunately the training script is missing. I am really stuck on this. would you like to help me by providing training script for the work?
my email: [email protected]

regards,
Asif

How about the code)

Training data open source

Hello.
I read your paper and the related research is very good, may I ask when the training part of the code can be open source