b'Ding Zhao'

Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main di...
06/19/2021 ∙ by Mengdi Xu, et al. ∙ 0 ∙ share

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CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

We present the first framework of Certifying Robust Policies for reinfor...
06/17/2021 ∙ by Fan Wu, et al. ∙ 0 ∙ share

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Semantically Controllable Scene Generation with Guidance of Explicit Knowledge

Deep Generative Models (DGMs) are known for their superior capability in...
06/08/2021 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles

Recent years have witnessed an increasing interest in improving the perc...
05/02/2021 ∙ by Sharad Chitlangia, et al. ∙ 0 ∙ share

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Personalized Keyphrase Detection using Speaker and Environment Information

In this paper, we introduce a streaming keyphrase detection system that ...
04/28/2021 ∙ by Rajeev Rikhye, et al. ∙ 0 ∙ share

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Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data

Optimal transport (OT) has generated much recent interest by its capabil...
02/07/2021 ∙ by Jiacheng Zhu, et al. ∙ 0 ∙ share

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Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

Safety is a critical concern when deploying reinforcement learning agent...
01/02/2021 ∙ by Baiming Chen, et al. ∙ 0 ∙ share

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Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method

This paper studies the safe reinforcement learning (RL) problem without ...
10/15/2020 ∙ by Zuxin Liu, et al. ∙ 0 ∙ share

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Rare-Event Simulation for Neural Network and Random Forest Predictors

We study rare-event simulation for a class of problems where the target ...
10/10/2020 ∙ by Yuanlu Bai, et al. ∙ 0 ∙ share

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Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Existing neural network-based autonomous systems are shown to be vulnera...
09/16/2020 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

Multi-agent navigation in dynamic environments is of great industrial va...
07/30/2020 ∙ by Zuxin Liu, et al. ∙ 0 ∙ share

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Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Evaluating the reliability of intelligent physical systems against rare ...
06/28/2020 ∙ by Mansur Arief, et al. ∙ 0 ∙ share

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Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Continuously learning to solve unseen tasks with limited experience has ...
06/19/2020 ∙ by Mengdi Xu, et al. ∙ 0 ∙ share

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Robust Unsupervised Learning of Temporal Dynamic Interactions

Robust representation learning of temporal dynamic interactions is an im...
06/18/2020 ∙ by Aritra Guha, et al. ∙ 0 ∙ share

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Dynamic Sparsity Neural Networks for Automatic Speech Recognition

In automatic speech recognition (ASR), model pruning is a widely adopted...
05/16/2020 ∙ by Zhaofeng Wu, et al. ∙ 0 ∙ share

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Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

Action and observation delays exist prevalently in the real-world cyber-...
05/11/2020 ∙ by Baiming Chen, et al. ∙ 0 ∙ share

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Delay-Aware Model-Based Reinforcement Learning for Continuous Control

Action delays degrade the performance of reinforcement learning in many ...
05/11/2020 ∙ by Baiming Chen, et al. ∙ 0 ∙ share

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A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

Thus far, end-to-end (E2E) models have not been shown to outperform stat...
03/28/2020 ∙ by Tara N. Sainath, et al. ∙ 0 ∙ share

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Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Long-tail and rare event problems become crucial when autonomous driving...
03/02/2020 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

Predicting surrounding vehicle behaviors are critical to autonomous vehi...
10/17/2019 ∙ by Jiacheng Zhu, et al. ∙ 0 ∙ share

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Recurrent Attentive Neural Process for Sequential Data

Neural processes (NPs) learn stochastic processes and predict the distri...
10/17/2019 ∙ by Shenghao Qin, et al. ∙ 0 ∙ share

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Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression

Generating multi-vehicle interaction scenarios can benefit motion planni...
10/08/2019 ∙ by Weiyang Zhang, et al. ∙ 0 ∙ share

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How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach

Proving ground has been a critical component in testing and validation f...
09/19/2019 ∙ by Rui Chen, et al. ∙ 0 ∙ share

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How to Evaluate Self-Driving Testing Ground? A Quantitative Approach

Testing ground has been a critical component in testing and validation f...
09/19/2019 ∙ by Rui Chen, et al. ∙ 0 ∙ share

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CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

Naturalistic driving trajectories are crucial for the performance of aut...
09/17/2019 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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Active Learning for Risk-Sensitive Inverse Reinforcement Learning

One typical assumption in inverse reinforcement learning (IRL) is that h...
09/14/2019 ∙ by Rui Chen, et al. ∙ 0 ∙ share

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A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction pattern...
07/17/2019 ∙ by Chengyuan Zhang, et al. ∙ 9 ∙ 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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Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation

Safety evaluation of autonomous vehicles is extensively studied recently...
04/19/2019 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequenc...
11/15/2018 ∙ by Yanzhang He, et al. ∙ 0 ∙ share

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Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

Considering its reliability to provide accurate 3D views along with prec...
09/16/2018 ∙ by Zuxin Liu, et al. ∙ 0 ∙ share

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Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters

Generating multi-vehicle trajectories analogous to these in real world c...
09/15/2018 ∙ by Wenhao Ding, et al. ∙ 0 ∙ share

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Synthesis of Different Autonomous Vehicles Test Approaches

Currently, the most prevalent way to evaluate an autonomous vehicle is t...
09/09/2018 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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An "Xcity" Optimization Approach to Designing Proving Grounds for Connected and Autonomous Vehicles

Proving ground, or on-track testing has been an essential part of testin...
08/09/2018 ∙ by Rui Chen, et al. ∙ 0 ∙ share

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Deep context: end-to-end contextual speech recognition

In automatic speech recognition (ASR) what a user says depends on the pa...
08/07/2018 ∙ by Golan Pundak, et al. ∙ 8 ∙ share

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Understanding V2V Driving Scenarios through Traffic Primitives

Semantically understanding complex drivers' encountering behavior, where...
07/27/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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Clustering of Driving Scenarios Using Connected Vehicle Datasets

Driving encounter classification and analysis can benefit autonomous veh...
07/23/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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An Optimal LiDAR Configuration Approach for Self-Driving Cars

LiDARs plays an important role in self-driving cars and its configuratio...
05/20/2018 ∙ by Shenyu Mou, et al. ∙ 0 ∙ share

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Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data

By using the onboard sensing and external connectivity technology, conne...
05/19/2018 ∙ by Yan Chang, et al. ∙ 0 ∙ share

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Fuel Economy and Emission Testing for Connected and Automated Vehicles Using Real-world Driving Datasets

By using the onboard sensing and external connectivity technology, conne...
05/19/2018 ∙ by Yan Chang, et al. ∙ 0 ∙ share

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A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms ha...
05/13/2018 ∙ by Jiacheng Zhu, et al. ∙ 0 ∙ share

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An Accelerated Approach to Safely and Efficiently Test Pre-produced Autonomous Vehicles on Public Streets

Various automobile and mobility companies, for instance, Ford, Uber, and...
05/05/2018 ∙ by Mansur Arief, et al. ∙ 0 ∙ share

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Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

Deep understanding of driving encounters could help self-driving cars ma...
02/28/2018 ∙ by Sisi Li, et al. ∙ 0 ∙ share

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Extracting V2V Encountering Scenarios from Naturalistic Driving Database

It is necessary to thoroughly evaluate the effectiveness and safety of C...
02/27/2018 ∙ by Zhaobin Mo, et al. ∙ 0 ∙ share

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Learning and Inferring a Driver's Braking Action in Car-Following Scenarios

Accurately predicting and inferring a driver's decision to brake is crit...
01/11/2018 ∙ by Wenshuo Wang, et al. ∙ 0 ∙ share

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A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

Evaluation and validation of complicated control systems are crucial to ...
10/01/2017 ∙ by Zhiyuan Huang, et al. ∙ 0 ∙ share

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Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications

Developing an automated vehicle, that can handle the complicated driving...
09/11/2017 ∙ by Wenshuo Wang, 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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Ding Zhao

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