Trajectory Prediction

165 papers with code • 27 benchmarks • 26 datasets

Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles.

Source: Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

Libraries

Use these libraries to find Trajectory Prediction models and implementations

Most implemented papers

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

agrimgupta92/sgan CVPR 2018

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.

GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

xincoder/GRIP arXiv preprint 2020

Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied.

From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video

JunweiLiang/Multiverse 20 Nov 2020

With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, socially-aware robot assistant and public safety monitoring.

Social NCE: Contrastive Learning of Socially-aware Motion Representations

vita-epfl/social-nce ICCV 2021

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

yadrimz/DESIRE CVPR 2017

DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.

Convolutional Social Pooling for Vehicle Trajectory Prediction

nachiket92/conv-social-pooling 15 May 2018

Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.

Unsupervised Traffic Accident Detection in First-Person Videos

MoonBlvd/tad-IROS2019 2 Mar 2019

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.

Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

StanfordASL/Trajectron-plus-plus ECCV 2020

Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.

It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction

HarshayuGirase/PECNet ECCV 2020

In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.

SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction

shuaishiliu/SGCN 4 Apr 2021

Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction.