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readme.md

The Trajectory Prediction Challenge of Apolloscapes Dataset

For detail and download

Introduction

This repository contains the evaluation scripts for the trajectory prediction challenge of the ApolloScapes dataset. Our trajectory dataset consists of camera-based images, LiDAR scanned point clouds, and manually annotated trajectories. It is collected under various lighting conditions and traffic densities in Beijing, China. More specifically, it contains highly complicated traffic flows mixed with vehicles, riders, and pedestrians.

Dataset download

sample_trajectory.zip sample_image.zip

prediction_test.zip prediction_train.zip

Evaluation

evaluation.py is the evaluation code. Run the code for a sample evaluation:

python evaluation.py --object_file=./test_eval_data/considered_objects.txt --gt_dir=./test_eval_data/prediction_gt.txt --res_file=./test_eval_data/prediction_result.txt
./test_eval_data/considered_objects.txt contains objects we consider when counting the error.
./test_eval_data/prediction_gt.txt is just for testing the code which is not the real ground truth. Please submit your result to the leaderboard to get true error.
./test_eval_data/prediction_result.txt is one example for submitted result.

Submission of data format

Submit your result for online evaluation here: Submit

Leaderboard: Leaderboard

Publication

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents. PDF BibTex

Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha.

AAAI(oral), 2019

@article{ma2018trafficpredict,
  title={TrafficPredict: Trajectory prediction for heterogeneous traffic-agents},
  author={Ma, Yuexin and Zhu, Xinge and Zhang, Sibo and Yang, Ruigang and Wang, Wenping and Manocha, Dinesh},
  journal={arXiv preprint arXiv:1811.02146},
  year={2018}
}

Contact

Please feel free to contact us, or raise an issue with any questions, suggestions or comments:

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