Data and replication package for the article "Assessing Microscopic Traffic Simulation Confidence Level Through Expert-Weighted AHP and Belief Theory: An Application to CAV Testing"

This paper presents an end-to-end approach for deploying and evaluating pedestrian crossing intention prediction on an on-board embedded computing platform. The proposed pipeline integrates YOLO11s for pedestrian detection, DeepSORT for identity tracking, RTMPose-s for pose estimation, an identity-aware temporal buffer, and PedAST-GCN for crossing intention prediction. We optimize the pipeline using ONNX Runtime and TensorRT and deploy it on an NVIDIA Jetson AGX Orin installed in KTH’s Research Concept Vehicle E. In addition, we introduce an offline LLM-based evaluation framework that automatically annotates pedestrian crossing states from real-world tracking videos and evaluates whether the system can anticipate crossing behavior one to two seconds in advance. Experiments conducted at the Gillinge test track demonstrate reliable prediction performance and real-time inference at approximately 16.6 FPS. This replication package provides the source code and documentation required to reproduce the deployment, optimization, and evaluation procedures reported in the paper.

This project develops an offline reinforcement learning framework for optimizing speed guidance of connected autonomous vehicles (CAVs) at signalized intersections under mixed traffic conditions. The framework learns safe and efficient policies from real-world trajectory data without requiring risky online interactions. The system employs Critic Regularized Regression (CRR) combined with real-world driving trajectories and Signal Phase and Timing (SPaT) information from the UCF-SST-CitySim dataset. This approach bridges the sim-to-real gap by learning directly from naturalistic human driving behavior while incorporating safety-critical features often neglected in prior work. 

Replication package for STAR: Structure-Aware Representation Learning for Efficient and Robust UAV-based Geo-Localization

This replication package includes the source codes of the pipeline, source data and results data, demo video, and the complementary material package (mentioned in the paper).