StanfordASL/Trajectron-plus-plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).
This project helps self-driving car engineers and robotics researchers predict the future paths of pedestrians, cyclists, and vehicles. It takes in historical movement data and environmental context, then outputs dynamically-feasible trajectory forecasts. The end user is typically a robotics engineer or researcher working on autonomous systems.
791 stars. No commits in the last 6 months.
Use this if you need to accurately forecast the movements of multiple interacting agents like cars and pedestrians in complex environments for autonomous driving or robotics applications.
Not ideal if you're looking for a simple, off-the-shelf solution without diving into model training and dataset preparation.
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Aug 17, 2023
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