Huei Peng

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University of Michigan

Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

It is essential for an automated vehicle in the field to perform discret...
04/18/2021 ∙ by Songan Zhang, et al. ∙ 0 ∙ share

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Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

This paper proposes a method to extract the position and pose of vehicle...
03/29/2021 ∙ by Minghan Zhu, et al. ∙ 0 ∙ share

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An Interaction-aware Evaluation Method for Highly Automated Vehicles

It is important to build a rigorous verification and validation (V V) ...
02/23/2021 ∙ by Xinpeng Wang, et al. ∙ 0 ∙ share

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Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning

Safeguard functions such as those provided by advanced emergency braking...
12/02/2020 ∙ by Zhong Cao, et al. ∙ 0 ∙ share

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Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

Object detection and tracking is a key task in autonomy. Specifically, 3...
11/04/2020 ∙ by Yuanxin Zhong, et al. ∙ 0 ∙ share

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SUPER: A Novel Lane Detection System

AI-based lane detection algorithms were actively studied over the last f...
05/14/2020 ∙ by Pingping Lu, et al. ∙ 0 ∙ share

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Monocular Depth Prediction Through Continuous 3D Loss

This paper reports a new continuous 3D loss function for learning depth ...
03/21/2020 ∙ by Minghan Zhu, et al. ∙ 23 ∙ share

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Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles

Deep reinforcement learning methods have been widely used in recent year...
03/18/2020 ∙ by Songan Zhang, et al. ∙ 0 ∙ share

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Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Reinforcement learning (RL) is attracting increasing interests in autono...
03/03/2020 ∙ by Lu Wen, et al. ∙ 0 ∙ share

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Mcity Data Collection for Automated Vehicles Study

The main goal of this paper is to introduce the data collection effort a...
12/12/2019 ∙ by Yiqun Dong, et al. ∙ 4 ∙ share

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Eco-Mobility-on-Demand Fleet Control with Ride-Sharing

Shared Mobility-on-Demand using automated vehicles can reduce energy con...
08/23/2019 ∙ by Xianan Huang, et al. ∙ 0 ∙ share

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Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Autonomous vehicles (AV) are expected to navigate in complex traffic sce...
06/25/2019 ∙ by Yaohui Guo, et al. ∙ 0 ∙ share

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Longitudinal Trajectory Prediction of Human-driven Vehicles Near Traffic Lights

Predicting future trajectories of human-driven vehicles is a crucial pro...
06/02/2019 ∙ by Geunseob Oh, et al. ∙ 0 ∙ share

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Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior

Automated vehicles can change the society by improved safety, mobility a...
08/01/2018 ∙ by Xianan Huang, et al. ∙ 0 ∙ share

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Eco-Routing based on a Data Driven Fuel Consumption Model

A nonparametric fuel consumption model is developed and used for eco-rou...
01/18/2018 ∙ by Xianan Huang, et al. ∙ 0 ∙ share

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Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers

The safety of Automated Vehicles (AVs) must be assured before their rele...
07/10/2016 ∙ by Ding Zhao, et al. ∙ 0 ∙ share

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Huei Peng

Featured Co-authors

Ding Zhao

Fan Wang

Wenshuo Wang

Henry Lam

Shengbo Eben Li

Ryan M. Eustice

Jingliang Duan

Maani Ghaffari

Jiacheng Zhu

Mansur Arief

Kuan-Hui Lee

Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

An Interaction-aware Evaluation Method for Highly Automated Vehicles

Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning

Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss

SUPER: A Novel Lane Detection System

Monocular Depth Prediction Through Continuous 3D Loss

Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles

Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization

Mcity Data Collection for Automated Vehicles Study

Eco-Mobility-on-Demand Fleet Control with Ride-Sharing

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

Longitudinal Trajectory Prediction of Human-driven Vehicles Near Traffic Lights

Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior

Eco-Routing based on a Data Driven Fuel Consumption Model

Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers