6 papers
CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization
Bo Wu, Ruoshen Mo, Justin Yue +6
Cellular vehicle-to-everything (C-V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However, existing datasets often over…
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
Siyan Li, Zehao Wang, Jiachen Li +3
Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler…
HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning
Ziyan Zhang, Changxin Wan, Peng Hao +5
This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs…
HeCoFuse: Cross-Modal Complementary V2X Cooperative Perception with Heterogeneous Sensors
Chuheng Wei, Ziye Qin, Walter Zimmer +2
Real-world Vehicle-to-Everything (V2X) cooperative perception systems often operate under heterogeneous sensor configurations due to cost constraints and deployment variability acr…
Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles
Chuheng Wei, Ziye Qin, Ziyan Zhang +2
Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental underst…
PDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior
Chuheng Wei, Ziye Qin, Siyan Li +7
Driving behavior is inherently personal, influenced by individual habits, decision-making styles, and physiological states. However, most existing datasets treat all drivers as hom…