Counterfactual learning on graphs: A survey
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 …
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap
Given the uncertainty surrounding how existing explainability methods for autonomous
vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is …
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
Artificial intelligence (AI) encompasses the development of systems that perform tasks
typically requiring human intelligence, such as reasoning and learning. Despite its …
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 …
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
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 …
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 …
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 …
Interpretable Responsibility Sharing (IRS), which enhances planning efficiency in domestic …
Multimodal vehicle trajectory prediction based on intention inference with lane graph representation
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 …
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
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 …
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
Virtual environments provide a rich and controlled setting for collecting detailed data on
human behavior, offering unique opportunities for predicting human trajectories in dynamic …
human behavior, offering unique opportunities for predicting human trajectories in dynamic …