Rahman, Quazi
A two-stage hybrid deep learning framework with reinforce-learned temporal dilated convolutions for predicting vehicle left-turn speed at pedestrian crossings
Predicting vehicle speed at critical road segments, such as pedestrian crossings during left-turn maneuvers at signalized intersections, is essential for improving traffic safety and supporting autonomous driving systems. This thesis presents a novel two-stage hybrid deep learning framework enhanced with reinforcement learning to forecast vehicle left-turn speed at pedestrian crossings.
Using a multivariate time series dataset of vehicle speed and acceleration, the final three seconds of data are intentionally removed to simulate real-world decision-making prior to reaching pedestrian crossings. In stage one, a Convolutional Neural Network (CNN) imputes the removed values. Stage two uses the imputed data to forecast speed, combining Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) networks as feature extractors, followed by a Random Forest Regressor (RFR) for robust speed predictions.
Reinforcement learning is employed to dynamically adjusts the TCN's dilation rate, improving temporal pattern capture. Experimental results show the proposed framework outperforms standalone, hybrid, and state-of-the-art models.
Author Keywords: Data Imputation, Dynamic Dilation, Left-Turn Maneuver, Reinforcement Learning, Temporal Convolutional Network, Time series Forecasting
Automated Grading of UML Use Case Diagrams
This thesis presents an approach for automated grading of UML Use Case diagrams. Many software engineering courses require students to learn how to model the behavioural features of a problem domain or an object-oriented design in the form of a use case diagram. Because assessing UML assignments is a time-consuming and labor-intensive operation, there is a need for an automated grading strategy that may help instructors by speeding up the grading process while also maintaining uniformity and fairness in large classrooms. The effectiveness of this automated grading approach was assessed by applying it to two real-world assignments. We demonstrate how the result is similar to manual grading, which was less than 7% on average; and when we applied some strategies, such as configuring settings and using multiple solutions, the average differences were even lower. Also, the grading methods and the tool are proposed and empirically validated.
Author Keywords: Automated Grading, Compare Models, Use Case