25 citations · 53 across the 21 of their papers we have counts for
22 papers
Silence Is Not Assurance: When Production Lane Keeping Assist Does Not Warn, and What It Costs
Tianjia Yang, Yuxin Ding, Ke Li +3
Production lane keeping assist (LKA) systems tell the driver whether they detect the lane markings, but whether this displayed status reflects what the system perceives, and what h…
VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving
Tianjia Yang, Ke Li, Ruwen Qin +1
Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the vehicle can perceive and what it…
CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation
Ke Li, Kaidi Liang, Yuxin Ding +3
Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. However, generating controllable safety-critical sc…
HMPDM: A Diffusion Model for Driving Video Prediction with Historical Motion Priors
Ke Li, Tianjia Yang, Kaidi Liang +2
Video prediction is a useful function for autonomous driving, enabling intelligent vehicles to reliably anticipate how driving scenes will evolve and thereby supporting reasoning a…
CrashChat: A Multimodal Large Language Model for Multitask Traffic Crash Video Analysis
Kaidi Liang, Ke Li, Xianbiao Hu +1
Automating crash video analysis is essential to leverage the growing availability of driving video data for traffic safety research and accountability attribution in autonomous dri…
Multi-label Scene Classification for Autonomous Vehicles: Acquiring and Accumulating Knowledge from Diverse Datasets
Ke Li, Chenyu Zhang, Yuxin Ding +2
Driving scenes are inherently heterogeneous and dynamic. Multi-attribute scene identification, as a high-level visual perception capability, provides autonomous vehicles (AVs) with…
Development of a Causal Model for Improving Rural Seniors' Accessibility: Data Evidences
Ke Li, Shizhe Li, Ruwen Qin
Seniors residing in rural areas often encounter limited accessibility to opportunities, resources, and services. This paper introduces a model proposing that both aging and rural r…
Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos
Muhammad Monjurul Karim, Ruwen Qin, Yinhai Wang
To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of sur…
Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection
Chenyu Zhang, Zhaozheng Yin, Ruwen Qin
Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection. This paper proposes an Attention-Enhanced…
A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images
Chenyu Zhang, Muhammad Monjurul Karim, Ruwen Qin
The vast network of bridges in the United States raises a high requirement for maintenance and rehabilitation. The massive cost of manual visual inspection to assess bridge conditi…
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos
Muhammad Monjurul Karim, Ruwen Qin, Zhaozheng Yin
Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard cameras (dashcams) is essential to facilitate safe navigation in a complex environment. Accident-…
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
Chenyu Zhang, Muhammad Monjurul Karim, Zhaozheng Yin +1
Aerial robots such as drones have been leveraged to perform bridge inspections. Inspection images with both recognizable structural elements and apparent surface defects can be col…
A Simulation Study of Passing Drivers' Responses to the Autonomous Truck-Mounted Attenuator System in Road Maintenance
Yu Li, Bill Wang, William Li +1
The Autonomous Truck-Mounted Attenuator (ATMA) system is a lead-follower vehicle system based on autonomous driving and connected vehicle technologies. The lead truck performs main…
A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection
Yu Li, Anisha Parsan, Bill Wang +3
Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specif…
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals
Wenjin Tao, Haodong Chen, Md Moniruzzaman +3
Human Activity Recognition (HAR) using wearable devices such as smart watches embedded with Inertial Measurement Unit (IMU) sensors has various applications relevant to our daily l…
A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos
Muhammad Monjurul Karim, Ruwen Qin, Zhaozheng Yin +1
Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technolog…
Crash Report Data Analysis for Creating Scenario-Wise, Spatio-Temporal Attention Guidance to Support Computer Vision-based Perception of Fatal Crash Risks
Yu Li, Muhammad Monjurul Karim, Ruwen Qin
Reducing traffic fatalities and serious injuries is a top priority of the US Department of Transportation. The computer vision (CV)-based crash anticipation in the near-crash phase…
A Virtual Reality-based Training and Assessment System for Bridge Inspectors with an Assistant Drone
Yu Li, Muhammad Monjurul Karim, Ruwen Qin
Over 600,000 bridges in the U.S. must be inspected every two years to identify flaws, defects, or potential problems that may need follow-up maintenance. Bridge inspection has adop…
A Gaze Data-based Comparative Study to Build a Trustworthy Human-AI Collaboration in Crash Anticipation
Yu Li, Muhammad Monjurul Karim, Ruwen Qin
Vehicles with a safety function for anticipating crashes in advance can enhance drivers' ability to avoid crashes. As dashboard cameras have become a low-cost sensor device accessi…
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents
Muhammad Monjurul Karim, Yu Li, Ruwen Qin
Traffic accident anticipation is a vital function of Automated Driving Systems (ADSs) for providing a safety-guaranteed driving experience. An accident anticipation model aims to p…
A system of vision sensor based deep neural networks for complex driving scene analysis in support of crash risk assessment and prevention
Muhammad Monjurul Karim, Yu Li, Ruwen Qin +1
To assist human drivers and autonomous vehicles in assessing crash risks, driving scene analysis using dash cameras on vehicles and deep learning algorithms is of paramount importa…
A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
Muhammad Monjurul Karim, Yu Li, Ruwen Qin +1
The rapid advancement of sensor technologies and artificial intelligence are creating new opportunities for traffic safety enhancement. Dashboard cameras (dashcams) have been widel…