ABSTRACT
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS.
FEATURES:
- Provides the necessary background for newcomers to the field (both academics and interested practitioners)
- Presents a timely snapshot of explainable and interpretable models in ITS applications
- Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS
- Identifies future research directions and open problems
TABLE OF CONTENTS
part I|30 pages
Toward Explainable ITS
chapter Chapter 1|29 pages
Explainable Artificial Intelligence for Intelligent Transportation Systems: Are We There Yet?
part II|224 pages
Interpretable Methods for ITS Applications
chapter Chapter 5|26 pages
Advances in Explainable Reinforcement Learning: An Intelligent Transportation Systems Perspective
chapter Chapter 8|18 pages
Intelligent Techniques and Explainable Artificial Intelligence for Vessel Traffic Service: A Survey
part III|20 pages
Ethical, Social, and Legal Implications of XAI in ITS