Counterfactual learning on graphs: A survey

Z Guo, T Xiao, Z Wu, C Aggarwal, H Liu… - arXiv preprint arXiv …, 2023 - arxiv.org
Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap

S Tekkesinoglu, A Habibovic, L Kunze - arXiv preprint arXiv:2404.00019, 2024 - arxiv.org
Given the uncertainty surrounding how existing explainability methods for autonomous
vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is …

[HTML][HTML] Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

SU Hamida, MJM Chowdhury, NR Chakraborty… - Big Data and Cognitive …, 2024 - mdpi.com
Artificial intelligence (AI) encompasses the development of systems that perform tasks
typically requiring human intelligence, such as reasoning and learning. Despite its …

Trajectory Prediction and Risk Assessment in Car-Following Scenarios Using a Noise-Enhanced Generative Adversarial Network

T Fu, X Li, J Wang, L Zhang, H Gong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Traditional conflict analysis methods, relying on the assumption of constant velocity, often
fall short in capturing the dynamic nature of driver behavior randomness during the …

Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness

DF Llorca, R Hamon, H Junklewitz, K Grosse… - arXiv preprint arXiv …, 2024 - arxiv.org
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous
Vehicles (AVs), examining the challenges introduced by AI components and the impact on …

Toward Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation: Interaction Awareness and Hierarchical Explainability

M Tabatabaie, S He, K Shin, H Wang - Journal on Autonomous …, 2024 - dl.acm.org
Understanding and learning the actor-to-X interactions (AXIs), such as those between the
focal vehicles (actor) and other traffic participants, such as other vehicles and pedestrians …

Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning

AS Yenicesu, S Nourmohammadi, B Cicek… - arXiv preprint arXiv …, 2024 - arxiv.org
This article introduces a novel heuristic for Task and Motion Planning (TAMP) named
Interpretable Responsibility Sharing (IRS), which enhances planning efficiency in domestic …

Multimodal vehicle trajectory prediction based on intention inference with lane graph representation

Y Chen, Y Zou, Y Xie, Y Zhang, J Tang - Expert Systems with Applications, 2025 - Elsevier
Accurately predicting the trajectories of nearby vehicles is a crucial and complex task in
autonomous driving due to the inherent uncertainty in driving behavior. Multimodal trajectory …

Interaction-Aware and Hierarchically-Explainable Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation

M Tabatabaie, S He, KG Shin - 2023 IEEE/RSJ International …, 2023 - ieeexplore.ieee.org
Understanding and learning the actor-to-X inter-actions (AXIs), such as those between the
focal vehicles (actor) and other traffic participants (eg, other vehicles, pedestrians) as well as …

DiVR: incorporating context from diverse VR scenes for human trajectory prediction

FF Gallo, HY Wu, L Sassatelli - arXiv preprint arXiv:2411.08409, 2024 - arxiv.org
Virtual environments provide a rich and controlled setting for collecting detailed data on
human behavior, offering unique opportunities for predicting human trajectories in dynamic …