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https://github.com/driving-behavior/DBNet

DBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018
https://github.com/driving-behavior/DBNet

autonomous-driving benchmark cvpr2018 dbnet driving-behavior point-cloud steering-wheel vehicle-speed

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DBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018

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README 

        



![db-prediction](docs/pred.jpg)



[DBNet](http://www.dbehavior.net/) is a __large-scale driving behavior dataset__, which provides large-scale __high-quality point clouds__ scanned by Velodyne lasers, __high-resolution videos__ recorded by dashboard cameras and __standard drivers' behaviors__ (vehicle speed, steering angle) collected by real-time sensors.



Extensive experiments demonstrate that extra depth information helps networks to determine driving policies indeed. We hope it will become useful resources for the autonomous driving research community.



_Created by [Yiping Chen*](https://scholar.google.com/citations?user=e9lv2fUAAAAJ&hl=en), [Jingkang Wang*](https://wangjksjtu.github.io/), [Jonathan Li](https://uwaterloo.ca/mobile-sensing/people-profiles/jonathan-li), [Cewu Lu](http://www.mvig.org/), Zhipeng Luo, HanXue and [Cheng Wang](http://chwang.xmu.edu.cn/). (*equal contribution)_



The resources of our work are available: [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_LiDAR-Video_Driving_Dataset_CVPR_2018_paper.pdf), [[code]](https://github.com/driving-behavior/DBNet), [[video]](http://www.dbehavior.net/data/demo.mp4), [[website]](http://www.dbehavior.net/), [[challenge]](http://www.dbehavior.net/task.html), [[prepared data]](https://drive.google.com/file/d/1WxzOrhvMnHCOkh6EFGWltflyPb_UnGqo/view?usp=sharing)



## Contents

1. [Introduction](#introduction)

2. [Requirements](#requirements)

3. [Quick Start](#quick-start)

4. [Baseline](#baseline)

5. [Contributors](#contributors)

6. [Citation](#citation)

7. [License](#license)



## Introduction

This work is based on our [research paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_LiDAR-Video_Driving_Dataset_CVPR_2018_paper.html), which appears in CVPR 2018. We propose a large-scale dataset for driving behavior learning, namely, DBNet. You can also check our [dataset webpage](http://www.dbehavior.net/) for a deeper introduction.



In this repository, we release __demo code__ and __partial prepared data__ for training with only images, as well as leveraging feature maps or point clouds. The prepared data are accessible [here](https://drive.google.com/open?id=14RPdVTwBTuCTo0tFeYmL_SyN8fD0g6Hc). (__More demo models and scripts are released soon!__)



## Requirements



* **Tensorflow 1.2.0**

* Python 2.7

* CUDA 8.0+ (For GPU)

* Python Libraries: numpy, scipy and __laspy__



The code has been tested with Python 2.7, Tensorflow 1.2.0, CUDA 8.0 and cuDNN 5.1 on Ubuntu 14.04. But it may work on more machines (directly or through mini-modification), pull-requests or test report are well welcomed.



## Quick Start

### Training

To train a model to predict vehicle speeds and steering angles:



    python train.py --model nvidia_pn --batch_size 16 --max_epoch 125 --gpu 0



The names of the models are consistent with our [paper](http://www.dbehavior.net/publications.html).

Log files and network parameters will be saved to `logs` folder in default.



To see HELP for the training script:



    python train.py -h



We can use TensorBoard to view the network architecture and monitor the training progress.



    tensorboard --logdir logs



### Evaluation    

After training, you could evaluate the performance of models using `evaluate.py`. To plot the figures or calculate AUC, you may need to have matplotlib library installed.



    python evaluate.py --model_path logs/nvidia_pn/model.ckpt



### Prediction

To get the predictions of test data:



    python predict.py



The results are saved in `results/results` (every segment) and `results/behavior_pred.txt` (merged) by default.

To change the storation location:



    python predict.py --result_dir specified_dir



The result directory will be created automatically if it doesn't exist.



## Baseline

MethodSettingAccuracyAUCMEAEAMEnvidia-pnVideos + Laser Pointsangle70.65% (<5)0.7799 29.464.2320.88speed82.21% (<3)0.870118.561.809.68



This baseline is run on __dbnet-2018 challenge data__ and only __nvidia\_pn__ is tested. To measure difficult architectures comprehensively, several metrics are set, including accuracy under different thresholds, area under curve (__AUC__), max error (__ME__), mean error (__AE__) and mean of max errors (__AME__).



The implementations of these metrics could be found in `evaluate.py`.



## Contributors

DBNet was developed by [MVIG](http://www.mvig.org/), Shanghai Jiao Tong University* and [SCSC](http://scsc.xmu.edu.cn/) Lab, Xiamen University* (*alphabetical order*).



## Citation

If you find our work useful in your research, please consider citing:



	@InProceedings{DBNet2018,

	  author = {Yiping Chen and Jingkang Wang and Jonathan Li and Cewu Lu and Zhipeng Luo and HanXue and Cheng Wang},

	  title = {LiDAR-Video Driving Dataset: Learning Driving Policies Effectively},

	  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

	  month = {June},

	  year = {2018}

	}



## License

Our code is released under Apache 2.0 License. The copyright of DBNet could be checked [here](http://www.dbehavior.net/contact.html).