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Pytorch implementation for "TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents"

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TrafficPredict

Pytorch implementation for the paper: TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (AAAI), Oral, 2019

The repo has been forked initially from Anirudh Vemula's repository for his paper Social Attention: Modeling Attention in Human Crowds (ICRA 2018). If you find this code useful in your research then please also cite Anirudh Vemula's paper.

Comparison of results:

Methods Paper ADE This repo ADE Paper FDE This repo FDE
pedestrian 0.091 0.088 0.150 0.132
bicycle 0.083 0.075 0.139 0.115
vehicle 0.080 0.090 0.131 0.153
total 0.085 0.084 0.141 0.133

Requirements

How to Run

  • First cd srnn
  • To train the model run python train.py (See the code to understand all the arguments that can be given to the command)
  • To test the model run python sample.py --epoch=n where n is the epoch at which you want to load the saved model. (See the code to understand all the arguments that can be given to the command)

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Pytorch implementation for "TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents"

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