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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...
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Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Safety is a critical concern when deploying reinforcement learning agent...
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Safe Model-based Reinforcement Learning with Robust Cross-Entropy Method
This paper studies the safe reinforcement learning (RL) problem without ...
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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 ...
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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...
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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...
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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 ...
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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 ...
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Robust Unsupervised Learning of Temporal Dynamic Interactions
Robust representation learning of temporal dynamic interactions is an im...
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Dynamic Sparsity Neural Networks for Automatic Speech Recognition
In automatic speech recognition (ASR), model pruning is a widely adopted...
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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-...
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Delay-Aware Model-Based Reinforcement Learning for Continuous Control
Action delays degrade the performance of reinforcement learning in many ...
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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...
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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...
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Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Predicting surrounding vehicle behaviors are critical to autonomous vehi...
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Recurrent Attentive Neural Process for Sequential Data
Neural processes (NPs) learn stochastic processes and predict the distri...
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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...
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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...
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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...
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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...
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Active Learning for Risk-Sensitive Inverse Reinforcement Learning
One typical assumption in inverse reinforcement learning (IRL) is that h...
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A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Semantic learning and understanding of multi-vehicle interaction pattern...
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Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
Autonomous vehicles (AV) are expected to navigate in complex traffic sce...
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Assessing Modeling Variability in Autonomous Vehicle Accelerated Evaluation
Safety evaluation of autonomous vehicles is extensively studied recently...
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Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequenc...
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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...
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Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters
Generating multi-vehicle trajectories analogous to these in real world c...
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Synthesis of Different Autonomous Vehicles Test Approaches
Currently, the most prevalent way to evaluate an autonomous vehicle is t...
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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...
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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...
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Understanding V2V Driving Scenarios through Traffic Primitives
Semantically understanding complex drivers' encountering behavior, where...
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Clustering of Driving Scenarios Using Connected Vehicle Datasets
Driving encounter classification and analysis can benefit autonomous veh...
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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...
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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...
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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...
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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...
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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...
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Clustering of Naturalistic Driving Encounters Using Unsupervised Learning
Deep understanding of driving encounters could help self-driving cars ma...
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Extracting V2V Encountering Scenarios from Naturalistic Driving Database
It is necessary to thoroughly evaluate the effectiveness and safety of C...
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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...
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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 ...
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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...
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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...
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