[HTML][HTML] Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models
In the field of autonomous vehicles (AVs), accurately discerning commander intent and
executing linguistic commands within a visual context presents a significant challenge. This …
executing linguistic commands within a visual context presents a significant challenge. This …
Explainable AI approaches in deep learning: Advancements, applications and challenges
Abstract Explainable Artificial Intelligence refers to developing artificial intelligence models
and systems that can provide clear, understandable, and transparent explanations for their …
and systems that can provide clear, understandable, and transparent explanations for their …
Dynamic urban traffic rerouting with fog‐cloud reinforcement learning
Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
[HTML][HTML] Human as AI mentor: Enhanced human-in-the-loop reinforcement learning for safe and efficient autonomous driving
Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
Artificial intelligence models encounter significant challenges due to their black-box nature,
particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles …
particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles …
Integrating big data analytics in autonomous driving: An unsupervised hierarchical reinforcement learning approach
In the realm of autonomous vehicular systems, there has been a notable increase in end-to-
end algorithms designed for complete self-navigation. Researchers are increasingly …
end algorithms designed for complete self-navigation. Researchers are increasingly …
Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system
Driver lane change intention (DLCI) predicting has become an essential research for the
development of human–machine co-driving system. This work makes an attempt to predict …
development of human–machine co-driving system. This work makes an attempt to predict …
XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving Systems
S Nazat, L Li, M Abdallah - IEEE Access, 2024 - ieeexplore.ieee.org
The advent of autonomous driving systems has given rise to pressing cybersecurity issues
regarding the vulnerability of vehicular ad hoc networks (VANETs) to potential attacks. This …
regarding the vulnerability of vehicular ad hoc networks (VANETs) to potential attacks. This …
Exploring explainable AI methods for bird sound-based species recognition systems
To recognize birds based on their calls, it would be helpful to have access to a machine-
learning system. Researchers use machine learning and artificial intelligence (AI) algorithms …
learning system. Researchers use machine learning and artificial intelligence (AI) algorithms …
A win-win relationship? New evidence on artificial intelligence and new energy vehicles
J Gu, Z Wu, Y Song, AC Nicolescu - Energy Economics, 2024 - Elsevier
Investigating the vital role of artificial intelligence is essential to develop the electric vehicle
market. This study utilises the wavelet-based QQR methodology to seize the dynamic …
market. This study utilises the wavelet-based QQR methodology to seize the dynamic …