64 citations · 165 across the 19 of their papers we have counts for
19 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…
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Juanwu Lu, Wei Zhan, Masayoshi Tomizuka +1
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…
Transferable and Adaptable Driving Behavior Prediction
Letian Wang, Yeping Hu, Liting Sun +3
While autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient, transferable, and…
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…
Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
Letian Wang, Yeping Hu, Changliu Liu
High fidelity behavior prediction of human drivers is crucial for efficient and safe deployment of autonomous vehicles, which is challenging due to the stochasticity, heterogeneity…
Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
Yeping Hu, Wei Zhan, Masayoshi Tomizuka
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically c…
Multi-agent Interactive Prediction under Challenging Driving Scenarios
Weihao Xuan, Ruijie Ren
In order to drive safely on the road, autonomous vehicle is expected to predict future outcomes of its surrounding environment and react properly. In fact, many researchers have be…
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
Jiachen Li, Wei Zhan, Yeping Hu +1
Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decis…
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
Yeping Hu, Liting Sun, Masayoshi Tomizuka
Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of…
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Liting Sun, Wei Zhan, Yeping Hu +1
Understanding human driving behavior is important for autonomous vehicles. In this paper, we propose an interpretable human behavior model in interactive driving scenarios based on…
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka +1
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road partic…
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
Yeping Hu, Wei Zhan, Liting Sun +1
For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels s…
A Framework for Probabilistic Generic Traffic Scene Prediction
Yeping Hu, Wei Zhan, Masayoshi Tomizuka
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of…
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Wei Zhan, Liting Sun, Yeping Hu +2
Autonomous vehicles should be able to generate accurate probabilistic predictions for uncertain behavior of other road users. Moreover, reactive predictions are necessary in highly…
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Yeping Hu, Wei Zhan, Masayoshi Tomizuka
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of…