Search Results for author:
Found 100 papers, 31 papers with code
A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model
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.
Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments
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
Optimization of Vehicle Connections in V2V-based Cooperative Localization
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
The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation
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
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
For projects that cost millions of dollars, it is critical to determine the right amount of data needed.
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
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.
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications
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.
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
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).
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
To overcome this problem, we extract naturalistic V2V encounters data from the database, and then separate the primary vehicle encounter category by clustering.
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged.
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
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.
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV).
Understanding V2V Driving Scenarios through Traffic Primitives
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.
Deep context: end-to-end contextual speech recognition
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
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing.
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition.
Evaluation Uncertainty in Data-Driven Self-Driving Testing
These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
We then use this model to reproduce the high-dimensional driving scenarios in a finitely tractable form.
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
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.
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
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.
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
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.
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
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.
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
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.
Recurrent Attentive Neural Process for Sequential Data
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs.
Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method
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.
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 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.
Delay-Aware Model-Based Reinforcement Learning for Continuous Control
Action delays degrade the performance of reinforcement learning in many real-world systems.
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
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
Robust Unsupervised Learning of Temporal Dynamic Interactions
These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance.
Rare-Event Simulation for Neural Network and Random Forest Predictors
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.
Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
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
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
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.
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
Functional optimal transport: map estimation and domain adaptation for functional data
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.
Personalized Keyphrase Detection using Speaker and Environment Information
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
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
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.
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness.
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
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.
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
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.
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems.
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication
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.
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
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.
Constrained Variational Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications.
A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation
Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
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.
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
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.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
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.
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling
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.
MHMS: Multimodal Hierarchical Multimedia Summarization
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.
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
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.
Prompting Decision Transformer for Few-Shot Policy Generalization
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
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.
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction
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.
Can Brain Signals Reveal Inner Alignment with Human Languages?
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.
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
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).
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
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
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
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.
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
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.
Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos
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.
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.
Continual Vision-based Reinforcement Learning with Group Symmetries
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.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles
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.
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
Multimodal image-text models have shown remarkable performance in the past few years.
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
Learning to View: Decision Transformers for Active Object Detection
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.
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
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.
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Such a model allows us to achieve always-on ambient speech recognition on edge devices with low-memory neural processors.
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies.
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
Hyper-Decision Transformer for Efficient Online Policy Adaptation
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.
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
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.
Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning
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.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Learning Shared Safety Constraints from Multi-task Demonstrations
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.
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
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.
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
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
COMPOSER: Scalable and Robust Modular Policies for Snake Robots
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.
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
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.
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization
This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively.
Creative Robot Tool Use with Large Language Models
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.
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
Structured Two-Stage True-Time-Delay Array Codebook Design for Multi-User Data Communication
Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users.
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing.
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning
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.
Learning from Sparse Offline Datasets via Conservative Density Estimation
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
Interpretation of Intracardiac Electrograms Through Textual Representations
Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography.
