Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation

08/06/2022
by   Juanwu Lu, et al.
0

Trajectory prediction is one of the essential tasks for autonomous vehicles. Recent progress in machine learning gave birth to a series of advanced trajectory prediction algorithms. Lately, the effectiveness of using graph neural networks (GNNs) with vectorized representations for trajectory prediction has been demonstrated by many researchers. Nonetheless, these algorithms either pay little attention to models' generalizability across various scenarios or simply assume training and test data follow similar statistics. In fact, when test scenarios are unseen or Out-of-Distribution (OOD), the resulting train-test domain shift usually leads to significant degradation in prediction performance, which will impact downstream modules and eventually lead to severe accidents. Therefore, it is of great importance to thoroughly investigate the prediction models in terms of their generalizability, which can not only help identify their weaknesses but also provide insights on how to improve these models. This paper proposes a generalizability analysis framework using feature attribution methods to help interpret black-box models. For the case study, we provide an in-depth generalizability analysis of one of the state-of-the-art graph-based trajectory predictors that utilize vectorized representation. Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems. Finally, we conclude the common prediction challenges and how weighting biases induced by the training process can deteriorate the accuracy.

READ FULL TEXT
research
05/29/2023

Improving the Generalizability of Trajectory Prediction Models with Frenet-Based Domain Normalization

Predicting the future trajectories of nearby objects plays a pivotal rol...
research
10/11/2021

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

Predicting the future trajectory of a moving agent can be easy when the ...
research
08/22/2023

Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions

This research aims to improve the accuracy of complex volleyball predict...
research
06/05/2023

Explaining and Adapting Graph Conditional Shift

Graph Neural Networks (GNNs) have shown remarkable performance on graph-...
research
05/24/2023

Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective

We investigate the question of whether the knowledge learned by graph ne...
research
04/04/2023

Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving

Accurate and robust trajectory predictions of road users are needed to e...
research
05/17/2021

Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings

We propose ArtSAGENet, a novel multimodal architecture that integrates G...

Please sign up or login with your details

Forgot password? Click here to reset