Abstract: Before entering cities and highways, autonomous vehicles must be thoroughly tested. Most autonomous vehicle evaluation methods are static and inefficient at generating challenging scenarios for tested vehicles. This paper proposes an adaptive evaluation framework…
Abstract: This paper presents an adaptive model-free deep reinforcement approach that can recognize and adapt to ride-sharing diurnal patterns with car-pooling. Deep Reinforcement Learning (RL) suffers from catastrophic forgetting because it is agnostic to experience…
Abstract: Chronic and common obstructive sleep apnea (OSA) has many comorbidities. Polysomnography (PSG), the gold standard for OSA diagnosis, is unavailable in many severe cases. Home-based OSA screening methods that identify high-risk patients who need…
Abstract: This paper introduces a Lasso Logistic Regression model based on feature-based time series data to assess COVID-19 disease severity and when to administer drugs or escalate intervention procedures. The dynamic feature-based classification model used…
Abstract: Re-ID via gait features in 3D skeleton sequences is a new topic with many benefits. Solutions use hand-crafted descriptors or supervised gait representation learning. This paper proposes a self-supervised gait encoding method to learn…
Abstract: ITS needs accurate and timely traffic flow prediction. Graph-based neural networks have improved prediction results. Graph construction and model time complexity remain challenges. This paper proposes a multi-stream feature fusion approach to extract and…
Abstract: Since some progressive MCI patients will develop Alzheimer’s disease, early diagnosis is crucial. A patch-based imaging-fed multi-stream deep convolutional neural network classifies stable and progressive MCI. First, we use a multivariate statistical test to…
Abstract: To improve urban transportation systems, transport mode identification (TMI) must infer user trajectories’ travel modes. Existing TMI methods use mobility features from densely sampled GPS trajectory points (1 second per GPS point) or IMU…
Abstract: Accurate spatio-temporal traffic forecasting underpins dynamic strategy and applications for intelligent transportation systems, improving traffic safety and reducing road congestion. Deep learning methods like CNN have improved traffic flow forecasting. These CNN-based methods learn…
Abstract: Real-time traffic demand can be revealed by accurate metro ridership forecasting. Deep learning algorithms are widely used to capture spatio-temporal features in metro riding behavior due to their superior performance. Current deep learning models…
Abstract: Autonomous vehicles, ridesharing, and self-driving technology will change how ride hailing platforms plan their services. However, advances in technology, road congestion, environmental concerns, fuel usage, vehicle emissions, and high vehicle usage costs have focused…
Abstract: Seizure prediction using graph theory and EEG signals is advanced. Recent deep learning approaches, which fail to fully explore EEG characterizations and electrode correlations simultaneously, neglect the spatial or temporal dependencies in an epileptic…
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