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Uncovering Interpretable Internal States of Merging Tasks at Highway On-Ramps for Autonomous Driving Decision-Making
Humans make daily-routine decisions based on their internal states in in...
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Fuzzing Based on Function Importance by Attributed Call Graph
Fuzzing has become one of the important methods for vulnerability detect...
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A Power Analysis of the Conditional Randomization Test and Knockoffs
In many scientific problems, researchers try to relate a response variab...
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On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic
Merging at highway on-ramps while interacting with other human-driven ve...
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Stratification and Optimal Resampling for Sequential Monte Carlo
Sequential Monte Carlo (SMC), also known as particle filters, has been w...
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Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Interpretation of common-yet-challenging interaction scenarios can benef...
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Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Understanding multi-vehicle interactive behaviors with temporal sequenti...
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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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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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Metropolized Knockoff Sampling
Model-X knockoffs is a wrapper that transforms essentially any feature i...
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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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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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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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Clustering of Naturalistic Driving Encounters Using Unsupervised Learning
Deep understanding of driving encounters could help self-driving cars ma...
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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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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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Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approach
Deep learning-based approaches have been widely used for training contro...
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Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation
Driving styles have a great influence on vehicle fuel economy, active sa...
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A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
A rapid pattern-recognition approach to characterize driver's curve-nego...
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