11 citations · 14 across the 6 of their papers we have counts for
6 papers
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Juanwu Lu, Wei Zhan, Masayoshi Tomizuka +1
Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieve…
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
Brian R. Bartoldson, Yeping Hu, Amar Saini +6
Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desi…
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation
Wei-Jer Chang, Chen Tang, Chenran Li +3
Traffic simulation plays a crucial role in evaluating and improving autonomous driving planning systems. After being deployed on public roads, autonomous vehicles need to interact…
Analyzing and Enhancing Closed-loop Stability in Reactive Simulation
Wei-Jer Chang, Yeping Hu, Chenran Li +2
Simulation has played an important role in efficiently evaluating self-driving vehicles in terms of scalability. Existing methods mostly rely on heuristic-based simulation, where t…
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction
Letian Wang, Yeping Hu, Liting Sun +3
When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and ada…
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction
Yeping Hu, Xiaogang Jia, Masayoshi Tomizuka +1
Accurately predicting possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically chang…