research
∙
09/07/2023
Total Variation Floodgate for Variable Importance Inference in Classification
Inferring variable importance is the key problem of many scientific stud...
share
research
∙
07/30/2023
Interactive Car-Following: Matters but NOT Always
Following a leading vehicle is a daily but challenging task because it r...
research
∙
07/07/2023
Calibrating Car-Following Models via Bayesian Dynamic Regression
Car-following behavior modeling is critical for understanding traffic fl...
research
∙
07/05/2023
Understanding Resolution of Multi-Language Bugs: An Empirical Study on Apache Projects
Background: In modern software systems, more and more systems are writte...
research
∙
10/15/2022
On Trustworthy Decision-Making Process of Human Drivers from the View of Perceptual Uncertainty Reduction
Humans are experts in making decisions for challenging driving tasks wit...
research
∙
09/24/2022
Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
Efficient and effective exploration in continuous space is a central pro...
research
∙
08/16/2022
Social Interactions for Autonomous Driving: A Review and Perspective
No human drives a car in a vacuum; she/he must negotiate with other road...
research
∙
06/07/2022
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
The booming of electric vehicles demands efficient battery disassembly f...
research
∙
04/28/2022
Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms
Computer vision algorithms have been prevalently utilized for 3-D road i...
research
∙
07/17/2021
Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix
The advance in machine learning (ML)-driven natural language process (NL...
research
∙
02/15/2021
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...
research
∙
10/07/2020
Fuzzing Based on Function Importance by Attributed Call Graph
Fuzzing has become one of the important methods for vulnerability detect...
research
∙
10/05/2020
A Power Analysis of the Conditional Randomization Test and Knockoffs
In many scientific problems, researchers try to relate a response variab...
research
∙
08/14/2020
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...
research
∙
04/04/2020
Stratification and Optimal Resampling for Sequential Monte Carlo
Sequential Monte Carlo (SMC), also known as particle filters, has been w...
research
∙
03/02/2020
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Interpretation of common-yet-challenging interaction scenarios can benef...
research
∙
10/28/2019
Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Understanding multi-vehicle interactive behaviors with temporal sequenti...
research
∙
10/17/2019
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Predicting surrounding vehicle behaviors are critical to autonomous vehi...
research
∙
10/17/2019
Recurrent Attentive Neural Process for Sequential Data
Neural processes (NPs) learn stochastic processes and predict the distri...
research
∙
10/08/2019
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
Generating multi-vehicle interaction scenarios can benefit motion planni...
research
∙
09/14/2019
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
One typical assumption in inverse reinforcement learning (IRL) is that h...
research
∙
07/17/2019
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Semantic learning and understanding of multi-vehicle interaction pattern...
research
∙
06/25/2019
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
Autonomous vehicles (AV) are expected to navigate in complex traffic sce...
research
∙
03/01/2019
Metropolized Knockoff Sampling
Model-X knockoffs is a wrapper that transforms essentially any feature i...
research
∙
09/15/2018
Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters
Generating multi-vehicle trajectories analogous to these in real world c...
research
∙
07/27/2018
Understanding V2V Driving Scenarios through Traffic Primitives
Semantically understanding complex drivers' encountering behavior, where...
research
∙
07/23/2018
Clustering of Driving Scenarios Using Connected Vehicle Datasets
Driving encounter classification and analysis can benefit autonomous veh...
research
∙
05/20/2018
An Optimal LiDAR Configuration Approach for Self-Driving Cars
LiDARs plays an important role in self-driving cars and its configuratio...
research
∙
05/13/2018
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
A multitude of publicly-available driving datasets and data platforms ha...
research
∙
02/28/2018
Clustering of Naturalistic Driving Encounters Using Unsupervised Learning
Deep understanding of driving encounters could help self-driving cars ma...
research
∙
01/11/2018
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
Accurately predicting and inferring a driver's decision to brake is crit...
research
∙
09/11/2017
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications
Developing an automated vehicle, that can handle the complicated driving...
research
∙
03/28/2017
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...
research
∙
06/03/2016
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...
research
∙
05/22/2016
