Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
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 …
the last two decades. There is increasing interest in the field as the deployment of …
[HTML][HTML] A Critical AI View on Autonomous Vehicle Navigation: The Growing Danger
Autonomous vehicles (AVs) represent a transformative advancement in transportation
technology, promising to enhance travel efficiency, reduce traffic accidents, and …
technology, promising to enhance travel efficiency, reduce traffic accidents, and …
[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 …
[HTML][HTML] Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
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 …
Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the
safety and efficiency of automated driving in highly interactive traffic environments …
safety and efficiency of automated driving in highly interactive traffic environments …
[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 …
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 …
New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records
Superior safety is the main banner value of promoting autonomous vehicle (AV) technology,
but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks …
but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks …
Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges
There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence
(XAI) approaches to boost people's confidence and trust in Artificial Intelligence methods …
(XAI) approaches to boost people's confidence and trust in Artificial Intelligence methods …