20 citations · 53 across the 9 of their papers we have counts for
9 papers
SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
Ning Ding, Ce Zhang, Azim Eskandarian
Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder auto…
Convolution-enhanced Evolving Attention Networks
Yujing Wang, Yaming Yang, Zhuo Li +7
Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series anal…
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
Ning Ding, Ce Zhang, Azim Eskandarian
A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even th…
X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA
Goodarz Mehr, Prasenjit Ghorai, Ce Zhang +4
Autonomous vehicles promise a future with a safer, cleaner, more efficient, and more reliable transportation system. However, the current approach to autonomy has focused on buildi…
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception
Ce Zhang, Azim Eskandarian
Reliable object detection using cameras plays a crucial role in enabling autonomous vehicles to perceive their surroundings. However, existing camera-based object detection approac…
Attention-based Neural Network for Driving Environment Complexity Perception
Ce Zhang, Azim Eskandarian, Xuelai Du
Environment perception is crucial for autonomous vehicle (AV) safety. Most existing AV perception algorithms have not studied the surrounding environment complexity and failed to i…
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification
Ce Zhang, Young-Keun Kim, Azim Eskandarian
Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural ne…
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
Ce Zhang, Azim Eskandarian
Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve…
A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
Ce Zhang, Azim Eskandarian
Drivers cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advan…