Search Results for author: Ding Zhao

Found 100 papers, 31 papers with code

Learning from Sparse Offline Datasets via Conservative Density Estimation

1 code implementation16 Jan 2024 Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao

Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.

D4RL Density Estimation +2

Gradient Shaping for Multi-Constraint Safe Reinforcement Learning

no code implementations23 Dec 2023 Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao

Leveraging insights from this framework and recognizing the significance of \textit{redundant} and \textit{conflicting} constraint conditions, we introduce the Gradient Shaping (GradS) method for general Lagrangian-based safe RL algorithms to improve the training efficiency in terms of both reward and constraint satisfaction.

reinforcement-learning Reinforcement Learning (RL) +1

RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios

no code implementations19 Dec 2023 Wenhao Ding, Yulong Cao, Ding Zhao, Chaowei Xiao, Marco Pavone

Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing.

Autonomous Vehicles In-Context Learning +1

Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

no code implementations31 Oct 2023 Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao

However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.

Autonomous Driving Decision Making +4

Structured Two-Stage True-Time-Delay Array Codebook Design for Multi-User Data Communication

no code implementations31 Oct 2023 Aditya Wadaskar, Ding Zhao, Ibrahim Pehlivan, Danijela Cabric

Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users.

Creative Robot Tool Use with Large Language Models

no code implementations19 Oct 2023 Mengdi Xu, Peide Huang, Wenhao Yu, Shiqi Liu, Xilun Zhang, Yaru Niu, Tingnan Zhang, Fei Xia, Jie Tan, Ding Zhao

This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.

Motion Planning Task and Motion Planning

Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization

no code implementations10 Oct 2023 Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held

This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively.

reinforcement-learning Reinforcement Learning (RL) +1

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models

no code implementations9 Oct 2023 Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor

Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e. g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data.

Continual Learning Imitation Learning

COMPOSER: Scalable and Robust Modular Policies for Snake Robots

no code implementations2 Oct 2023 Yuyou Zhang, Yaru Niu, Xingyu Liu, Ding Zhao

Instead of perceiving the hyper-redundancy and flexibility of snake robots as mere challenges, there lies an unexplored potential in leveraging these traits to enhance robustness and generalizability at the control policy level.

Multi-agent Reinforcement Learning

Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

no code implementations29 Sep 2023 Weiran Wang, Zelin Wu, Diamantino Caseiro, Tsendsuren Munkhdalai, Khe Chai Sim, Pat Rondon, Golan Pundak, Gan Song, Rohit Prabhavalkar, Zhong Meng, Ding Zhao, Tara Sainath, Pedro Moreno Mengibar

Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations

1 code implementation22 Sep 2023 Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space.

Autonomous Driving

Learning Shared Safety Constraints from Multi-task Demonstrations

1 code implementation NeurIPS 2023 Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu

Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect.

Continuous Control

Datasets and Benchmarks for Offline Safe Reinforcement Learning

3 code implementations15 Jun 2023 Zuxin Liu, Zijian Guo, Haohong Lin, Yihang Yao, Jiacheng Zhu, Zhepeng Cen, Hanjiang Hu, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao

This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.

Autonomous Driving Benchmarking +4

Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning

no code implementations15 Jun 2023 Miao Li, Wenhao Ding, Ding Zhao

The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models.

Decision Making Federated Learning +3

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

no code implementations18 May 2023 Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone

Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.

Offline RL reinforcement-learning

Hyper-Decision Transformer for Efficient Online Policy Adaptation

no code implementations17 Apr 2023 Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan

To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.

Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics

no code implementations4 Feb 2023 Jiacheng Zhu, JieLin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao

Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.

Data Augmentation

Learning to View: Decision Transformers for Active Object Detection

no code implementations23 Jan 2023 Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen

Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment.

Active Object Detection Motion Planning +5

No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles

no code implementations8 Dec 2022 Wenhao Yu, Chengxiang Zhao, Jiaxin Liu, Yingkai Yang, Xiaohan Ma, Jun Li, Weida Wang, Hong Wang, Ding Zhao, Xiaosong Hu

To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles.

Autonomous Driving Decision Making

Continual Vision-based Reinforcement Learning with Group Symmetries

no code implementations21 Oct 2022 Shiqi Liu, Mengdi Xu, Piede Huang, Yongkang Liu, Kentaro Oguchi, Ding Zhao

Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.

Autonomous Driving reinforcement-learning +1

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

1 code implementation18 Oct 2022 Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao

Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.

Domain Adaptation reinforcement-learning +1

LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos

no code implementations12 Oct 2022 JieLin Qiu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Ding Zhao, Hailin Jin

Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials.

Marketing Segmentation

Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

no code implementations10 Oct 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.

Robustness Certification of Visual Perception Models via Camera Motion Smoothing

1 code implementation4 Oct 2022 Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao

To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception.

Image Classification

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

no code implementations16 Sep 2022 Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao

A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments.

reinforcement-learning Reinforcement Learning (RL)

Federated Pruning: Improving Neural Network Efficiency with Federated Learning

no code implementations14 Sep 2022 Rongmei Lin, Yonghui Xiao, Tien-Ju Yang, Ding Zhao, Li Xiong, Giovanni Motta, Françoise Beaufays

Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions

no code implementations8 Sep 2022 Chulin Xie, Zhong Cao, Yunhui Long, Diange Yang, Ding Zhao, Bo Li

However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).

Autonomous Vehicles

Can Brain Signals Reveal Inner Alignment with Human Languages?

1 code implementation10 Aug 2022 William Han, JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao

In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.

EEG Relation +1

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

no code implementations2 Aug 2022 Jiacheng Zhu, JieLin Qiu, Zhuolin Yang, Douglas Weber, Michael A. Rosenberg, Emerson Liu, Bo Li, Ding Zhao

In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.

Data Augmentation

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

1 code implementation19 Jul 2022 Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations.

Causal Discovery reinforcement-learning +1

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

1 code implementation29 May 2022 Zuxin Liu, Zijian Guo, Zhepeng Cen, huan zhang, Jie Tan, Bo Li, Ding Zhao

One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.

Adversarial Attack reinforcement-learning +2

MHMS: Multimodal Hierarchical Multimedia Summarization

no code implementations7 Apr 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.

Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling

no code implementations4 Apr 2022 Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuang Huang, Henry Lam, Bo Li, Ding Zhao

In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative.

Autonomous Vehicles

PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

1 code implementation19 Mar 2022 Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, XuanLong Nguyen, Shirley You Ren

The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.

counterfactual Heart Rate Variability +1

COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

1 code implementation ICLR 2022 Fan Wu, Linyi Li, Chejian Xu, huan zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li

We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties.

Offline RL reinforcement-learning +1

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

no code implementations19 Feb 2022 Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao

In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.

reinforcement-learning Reinforcement Learning (RL)

A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation

no code implementations1 Feb 2022 Zhao Zhang, Ding Zhao, Xianfeng Terry Yang

Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.

BIG-bench Machine Learning

Constrained Variational Policy Optimization for Safe Reinforcement Learning

2 code implementations28 Jan 2022 Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao

Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications.

reinforcement-learning Reinforcement Learning (RL) +1

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

no code implementations25 Jan 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao

In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.

Data Augmentation

Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication

no code implementations26 Nov 2021 Xianxin Song, Ding Zhao, Haocheng Hua, Tony Xiao Han, Xun Yang, Jie Xu

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked.

Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

1 code implementation3 Nov 2021 Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, Ding Zhao

Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.

CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation

no code implementations26 Oct 2021 Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao

Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems.

Autonomous Driving Scene Generation

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

1 code implementation19 Jun 2021 Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

2 code implementations ICLR 2022 Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li

We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification.

Atari Games Autonomous Vehicles +2

Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance

no code implementations8 Jun 2021 Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao

Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness.

Autonomous Driving Point Cloud Segmentation +1

Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

1 code implementation CVPR 2022 Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao

To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects.

3D Object Detection Autonomous Driving +2

Personalized Keyphrase Detection using Speaker and Environment Information

no code implementations28 Apr 2021 Rajeev Rikhye, Quan Wang, Qiao Liang, Yanzhang He, Ding Zhao, Yiteng, Huang, Arun Narayanan, Ian McGraw

In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Functional optimal transport: map estimation and domain adaptation for functional data

1 code implementation7 Feb 2021 Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao

We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.

Domain Adaptation Transfer Learning

SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

1 code implementation9 Nov 2020 Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Jiacheng Zhu, Zuxin Liu, Wenhao Ding, Ding Zhao, Hesheng Wang

Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem.

Autonomous Driving Depth Estimation +4

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

1 code implementation15 Oct 2020 Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.

Model-based Reinforcement Learning Model Predictive Control +3

Rare-Event Simulation for Neural Network and Random Forest Predictors

no code implementations10 Oct 2020 Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.

BIG-bench Machine Learning

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

no code implementations16 Sep 2020 Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao

Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance.

Decision Making

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

no code implementations30 Jul 2020 Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao

Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.

reinforcement-learning Reinforcement Learning (RL)

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems

2 code implementations28 Jun 2020 Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao

Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.

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

1 code implementation NeurIPS 2020 Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao

We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.

Continual Learning Decision Making +6

Robust Unsupervised Learning of Temporal Dynamic Interactions

no code implementations18 Jun 2020 Aritra Guha, Rayleigh Lei, Jiacheng Zhu, XuanLong Nguyen, Ding Zhao

These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.

Representation Learning

Dynamic Sparsity Neural Networks for Automatic Speech Recognition

no code implementations16 May 2020 Zhaofeng Wu, Ding Zhao, Qiao Liang, Jiahui Yu, Anmol Gulati, Ruoming Pang

In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource constraints.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

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

1 code implementation11 May 2020 Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao

We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.

Autonomous Vehicles Multi-agent Reinforcement Learning +2

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

no code implementations28 Mar 2020 Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.

Sentence

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

no code implementations2 Mar 2020 Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao

We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated.

Autonomous Driving

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process

no code implementations17 Oct 2019 Jiacheng Zhu, Shenghao Qin, Wenshuo Wang, Ding Zhao

Constructed by incorporating NPs with recurrent neural networks (RNNs), the ARNP model predicts the distribution of a target vehicle trajectory conditioned on the observed long-term sequential data of all surrounding vehicles.

Autonomous Vehicles Meta-Learning +1

Recurrent Attentive Neural Process for Sequential Data

no code implementations17 Oct 2019 Shenghao Qin, Jiacheng Zhu, Jimmy Qin, Wenshuo Wang, Ding Zhao

Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs.

Autonomous Driving

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

no code implementations8 Oct 2019 Weiyang Zhang, Wenshuo Wang, Ding Zhao

The experimental results demonstrate that our proposed method can generate a bunch of human-like multi-vehicle interaction trajectories that can fit different road conditions remaining the key interaction patterns of agents in the provided scenarios, which is import to the development of autonomous vehicles.

Autonomous Vehicles Decision Making +2

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

1 code implementation17 Sep 2019 Wenhao Ding, Mengdi Xu, Ding Zhao

However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms.

Autonomous Driving Trajectory Prediction

Active Learning for Risk-Sensitive Inverse Reinforcement Learning

no code implementations14 Sep 2019 Rui Chen, Wenshuo Wang, Zirui Zhao, Ding Zhao

One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution.

Active Learning reinforcement-learning +1

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

1 code implementation17 Jul 2019 Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Ding Zhao

Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions.

Autonomous Vehicles

Evaluation Uncertainty in Data-Driven Self-Driving Testing

no code implementations19 Apr 2019 Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao

These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.

Autonomous Vehicles

A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters

no code implementations15 Sep 2018 Wenhao Ding, Wenshuo Wang, Ding Zhao

Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing.

Autonomous Vehicles Disentanglement

Deep context: end-to-end contextual speech recognition

no code implementations7 Aug 2018 Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, Ding Zhao

Our approach, which we re- fer to as Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR components along with embeddings of the context n-grams.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Understanding V2V Driving Scenarios through Traffic Primitives

no code implementations27 Jul 2018 Wenshuo Wang, Weiyang Zhang, Ding Zhao

Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design.

Clustering Decision Making +1

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

no code implementations13 May 2018 Jiacheng Zhu, Wenshuo Wang, Ding Zhao

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV).

Autonomous Vehicles Time Series +1

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

no code implementations5 May 2018 Mansur Arief, Peter Glynn, Ding Zhao

Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads.

Autonomous Vehicles

Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning

no code implementations28 Feb 2018 Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao

Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged.

Clustering Self-Driving Cars

Extraction of V2V Encountering Scenarios from Naturalistic Driving Database

no code implementations27 Feb 2018 Zhaobin Mo, Sisi Li, Diange Yang, Ding Zhao

To overcome this problem, we extract naturalistic V2V encounters data from the database, and then separate the primary vehicle encounter category by clustering.

Clustering Dynamic Time Warping

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

no code implementations11 Jan 2018 Wenshuo Wang, Junqiang Xi, Ding Zhao

A learning-based inference method, using onboard data from CAN-Bus, radar and cameras as explanatory variables, is introduced to infer drivers' braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM).

Specificity

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

no code implementations1 Oct 2017 Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao

The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.

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

no code implementations11 Sep 2017 Wenshuo Wang, Ding Zhao

Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data.

Autonomous Vehicles Time Series Analysis

Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

no code implementations16 Aug 2017 Wenshuo Wang, Junqiang Xi, Ding Zhao

In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns.

Time Series Analysis

How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior

no code implementations23 Jun 2017 Wenshuo Wang, Chang Liu, Ding Zhao

For projects that cost millions of dollars, it is critical to determine the right amount of data needed.

Density Estimation

The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation

no code implementations1 May 2017 Macheng Shen, Jing Sun, Ding Zhao

It has been shown, in our previous work, that the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints.

Systems and Control

Optimization of Vehicle Connections in V2V-based Cooperative Localization

no code implementations26 Mar 2017 Macheng Shen, Jing Sun, Ding Zhao

Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy.

Systems and Control

Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments

no code implementations19 Feb 2017 Macheng Shen, Ding Zhao, Jing Sun, Huei Peng

A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions.

Systems and Control

A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model

no code implementations4 Feb 2017 Wenshuo Wang, Ding Zhao, Junqiang Xi, Wei Han

Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB.

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