Search Results for author: Yeping Hu

Found 14 papers, 0 papers with code

Analyzing and Enhancing Closed-loop Stability in Reactive Simulation

no code implementations9 Aug 2022 Wei-Jer Chang, Yeping Hu, Chenran Li, Wei Zhan, Masayoshi Tomizuka

In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.

Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation

no code implementations6 Aug 2022 Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu

Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.

Autonomous Vehicles Trajectory Prediction

Transferable and Adaptable Driving Behavior Prediction

no code implementations10 Feb 2022 Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu

By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.

Autonomous Vehicles Trajectory Prediction

Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction

no code implementations9 Dec 2021 Letian Wang, Yeping Hu, Changliu Liu

With the feedback of the observed trajectory, the algorithm is applied to neural-network-based models to improve the performance of driving behavior predictions across different human subjects and scenarios.

Autonomous Vehicles

Causal-based Time Series Domain Generalization for Vehicle Intention Prediction

no code implementations3 Dec 2021 Yeping Hu, Xiaogang Jia, Masayoshi Tomizuka, Wei Zhan

In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed.

Autonomous Vehicles Domain Generalization +2

Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction

no code implementations7 Apr 2020 Yeping Hu, Wei Zhan, Masayoshi Tomizuka

In fact, the limitation to certain scenario is mainly due to the lackness of generic representations of the environment.

Autonomous Driving Navigate

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

no code implementations23 Jul 2019 Yeping Hu, Liting Sun, Masayoshi Tomizuka

Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.

Autonomous Vehicles

Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory

no code implementations19 Jul 2019 Liting Sun, Wei Zhan, Yeping Hu, Masayoshi Tomizuka

Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.

Autonomous Vehicles Decision Making

Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios

no code implementations12 Apr 2019 Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka, Kikuo Fujimura

In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.

Autonomous Vehicles Common Sense Reasoning +2

Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model

no code implementations22 Mar 2019 Yeping Hu, Wei Zhan, Liting Sun, Masayoshi Tomizuka

The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.

A Framework for Probabilistic Generic Traffic Scene Prediction

no code implementations30 Oct 2018 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.

Autonomous Vehicles Decision Making +1

Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios

no code implementations10 Sep 2018 Wei Zhan, Liting Sun, Yeping Hu, Jiachen Li, Masayoshi Tomizuka

Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.

Autonomous Vehicles Decision Making

Probabilistic Prediction of Vehicle Semantic Intention and Motion

no code implementations10 Apr 2018 Yeping Hu, Wei Zhan, Masayoshi Tomizuka

Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles.

Autonomous Vehicles motion prediction

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