25 citations · 53 across the 21 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…