[HTML][HTML] A taxonomy for autonomous vehicles considering ambient road infrastructure
To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …
Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems
User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …
Inaction: Interpretable action decision making for autonomous driving
Autonomous driving has attracted interest for interpretable action decision models that mimic
human cognition. Existing interpretable autonomous driving models explore static human …
human cognition. Existing interpretable autonomous driving models explore static human …
[PDF][PDF] Development and testing of an image transformer for explainable autonomous driving systems
Purpose-Perception has been identified as the main cause underlying most autonomous
vehicle related accidents. As the key technology in perception, deep learning (DL) based …
vehicle related accidents. As the key technology in perception, deep learning (DL) based …
Explainable artificial intelligence to detect image spam using convolutional neural network
Image spam threat detection has continually been a popular area of research with the
internet's phenomenal expansion. This research presents an explainable framework for …
internet's phenomenal expansion. This research presents an explainable framework for …
Nle-dm: Natural-language explanations for decision making of autonomous driving based on semantic scene understanding
Y Feng, W Hua, Y Sun - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …
research progress of autonomous driving. However, deep neural network is like a black box …
[HTML][HTML] Prospective Role of Foundation Models in Advancing Autonomous Vehicles
J Wu, B Gao, J Gao, J Yu, H Chu, Q Yu, X Gong… - Research, 2024 - spj.science.org
With the development of artificial intelligence and breakthroughs in deep learning, large-
scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc …
scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc …
Leveraging Driver Attention for an End-to-End Explainable Decision-Making From Frontal Images
Explaining the decision made by end-to-end autonomous driving is a difficult task. These
approaches take raw sensor data and compute the decision as a black box with large deep …
approaches take raw sensor data and compute the decision as a black box with large deep …
Deep Reinforcement Learning Based Framework for Mobile Energy Disseminator Dispatching to Charge On-the-Road Electric Vehicles
The exponential growth of electric vehicles (EVs) presents novel challenges in preserving
battery health and in addressing the persistent problem of vehicle range anxiety. To address …
battery health and in addressing the persistent problem of vehicle range anxiety. To address …
[HTML][HTML] A framework for lane-change maneuvers of connected autonomous vehicles in a mixed-traffic environment
In the transition era towards connected autonomous vehicles (CAVs), the sharing of the
roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected …
roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected …