Social LSTM implementation in PyTorch

[ITS'21] Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

This is the code base for our ACM CSCS 2019 paper: "RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs". This codebase contains implementations for several trajectory prediction methods including Social-GAN and TraPHic.

Social LSTM using PyTorch for Vehicle Data

PyTorch implementation for Social-LSTM, which is built to predict multi-vessel trajectories.

Using PINN based MPC for motion planning for SDC and LSTM for pedestrain's trajectory prediction as dynamic obstacles

We have compared 4 models- Vanilla LSTM, Social LSTM, OLSTM, and GRU to show their comparison for predicting non linear trajectories of pedestrians in different scenes. We demonstrate their performance on publically available datasets. We show how it is important to take into account the surroundings of the pedestrians to have a better accuracy.

We have compared 4 models- Vanilla LSTM, Social LSTM, OLSTM, and GRU to show their comparison for predicting non linear trajectories of pedestrians in different scenes. We demonstrate their performance on publically available datasets. We show how it is important to take into account the surroundings of the pedestrians to have a better accuracy

Intelligent Robot Navigation in Crowded Environments