5 papers
Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving
Yue Yao
This thesis addresses fundamental challenges in traffic scene prediction for autonomous driving by introducing robust and computationally efficient models based on polynomial repre…
EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations
Yue Yao, Mohamed-Khalil Bouzidi, Daniel Goehring +1
As the prediction horizon increases, predicting the future evolution of traffic scenes becomes increasingly difficult due to the multi-modal nature of agent motion. Most state-of-t…
An Empirical Bayes Analysis of Object Trajectory Representation Models
Yue Yao, Daniel Goehring, Joerg Reichardt
Linear trajectory models provide mathematical advantages to autonomous driving applications such as motion prediction. However, linear models' expressive power and bias for real-wo…
Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness
Yue Yao, Daniel Goehring, Joerg Reichardt
We study the Out-of-Distribution (OoD) generalization ability of three SotA trajectory prediction models with comparable In-Distribution (ID) performance but different model design…
Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations
Yue Yao, Shengchao Yan, Daniel Goehring +2
Robustness against Out-of-Distribution (OoD) samples is a key performance indicator of a trajectory prediction model. However, the development and ranking of state-of-the-art (SotA…