Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions

S Atakishiyev, M Salameh, H Yao, R Goebel - IEEE Access, 2024 - ieeexplore.ieee.org
Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

TimelyTale: A Multimodal Dataset Approach to Assessing Passengers' Explanation Demands in Highly Automated Vehicles

G Kim, S Hwang, M Seong, D Yeo, D Rus… - Proceedings of the ACM …, 2024 - dl.acm.org
Explanations in automated vehicles enhance passengers' understanding of vehicle decision-
making, mitigating negative experiences by increasing their sense of control. These …

[HTML][HTML] Visualizing imperfect situation detection and prediction in automated vehicles: Understanding users' perceptions via user-chosen scenarios

P Jansen, M Colley, T Pfeifer, E Rukzio - Transportation Research Part F …, 2024 - Elsevier
User acceptance is essential for successfully introducing automated vehicles (AVs).
Understanding the technology is necessary to overcome skepticism and achieve …

User Experience Evaluation Methods in Mixed Reality Environments

M García, J Requesens, S Cano - International Conference on Human …, 2024 - Springer
Interest in the metaverse and telepresence has sparked technological advancements,
enabling us to extend our perceived reality with off-the-shelf devices. Particularly, we shall …

Physiological Indices to Predict Driver Situation Awareness in VR

G Kim, J Lee, D Yeo, E An, SJ Kim - … of the 2023 ACM International Joint …, 2023 - dl.acm.org
Understanding drivers' states is essential for providing personalized interventions and
adaptive feedback in vehicles, thereby ensuring safer driving and a more comfortable driver …

Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

S Atakishiyev, M Salameh, R Goebel - arXiv preprint arXiv:2403.12176, 2024 - arxiv.org
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing
development of highly autonomous vehicles, largely due to advances in deep learning, the …

[HTML][HTML] A Testing and Evaluation Method for the Car-Following Models of Automated Vehicles Based on Driving Simulator

Y Zhang, Y Shao, X Shi, Z Ye - Systems, 2024 - mdpi.com
The continuous advancement of connected and automated driving technologies has
garnered considerable public attention regarding the safety and reliability of automated …

Adaptive In-Vehicle Virtual Reality for Reducing Motion Sickness: Manipulating Passenger Posture During Driving Events

A Elsharkawy, A Ataya, D Yeo, M Seong… - Companion of the 2024 …, 2024 - dl.acm.org
The rise of autonomous vehicles (AVs) has promoted the adoption of in-vehicle virtual reality
(VR) for creating immersive experiences. However, these experiences can trigger motion …

Trust Development and Explainability: A Longitudinal Study with a Personalized Assistive System

S Zafari, J de Pagter, G Papagni, A Rosenstein… - Multimodal …, 2024 - mdpi.com
This article reports on a longitudinal experiment in which the influence of an assistive
system's malfunctioning and transparency on trust was examined over a period of seven …

Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles

S Atakishiyev, M Salameh, R Goebel - arXiv preprint arXiv:2404.07383, 2024 - arxiv.org
Autonomous vehicles often make complex decisions via machine learning-based predictive
models applied to collected sensor data. While this combination of methods provides a …