9 papers
Neural POS Tagging for the Low-Resource Saraiki
Usman Nawaz, Muhammad Junaid Iqbal, Muhammad Asaf +4
Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications. However, Saraiki, written fr…
Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data
Zhihao Zhang, Keith Redmill, Chengyang Peng +1
A driving algorithm that aligns with good human driving practices, or at the very least collaborates effectively with human drivers, is crucial for developing safe and efficient au…
SignRAG: A Retrieval-Augmented System for Scalable Zero-Shot Road Sign Recognition
Minghao Zhu, Zhihao Zhang, Anmol Sidhu +1
Automated road sign recognition is a critical task for intelligent transportation systems, but traditional deep learning methods struggle with the sheer number of sign classes and…
An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Zhihao Zhang, Chengyang Peng, Minghao Zhu +2
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining ro…
Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
Zhihao Zhang, Chengyang Peng, Ekim Yurtsever +1
Automated vehicle control using reinforcement learning (RL) has attracted significant attention due to its potential to learn driving policies through environment interaction. Howe…
Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Chengyang Peng, Zhihao Zhang, Shiting Gong +3
Safe and real-time navigation is fundamental for humanoid robot applications. However, existing bipedal robot navigation frameworks often struggle to balance computational efficien…
Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks
Sarah Al-Shareeda, Fusun Ozguner, Keith Redmill +2
Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communicat…
Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
Zhihao Zhang, Ekim Yurtsever, Keith A. Redmill
Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, De…
Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles
Minghao Zhu, Anmol Sidhu, Keith A. Redmill
This paper focuses on safety performance testing and characterization of black-box highly automated vehicles (HAV). Existing testing approaches typically obtain the testing outcome…