Explainable AI for Intelligent transportation systems / edited by Amina Adadi, Afaf Bouhoute.

Other author Adadi, Amina.
Other author Bouhoute, Afaf.
Format Electronic
Publication InfoBoca Raton : CRC Press, 2024.
Descriptionpages cm
Supplemental ContentFull text available from eBooks on EBSCOhost
Subjects
Intelligent transportation systems.
Artificial intelligence.

Contents Explainable AI for Intelligent Transportation Systems : are we there yet? / Amina Adadi, Afaf Bouhoute -- Towards safe, explainable, and regulated autonomous driving / Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel -- Explainable machine learning method for predicting road traffic accident injury severity in Addis Ababa city based on a new graph feature selection technique / Yassine Akhiat, Younes Bouchlaghem, Ahmed Zinedine, Mohamed Chahhou -- COVID-19 pandemic effects on traffic crash patterns and injuries in Barcelona, Spain : an interpretable approach / Ahmad Aiash , Francesc Robuste -- Advances in explainable reinforcement learning : an intelligent transportation systems perspective / Rudy Milani, Maximilian Moll, Stefan Pickl -- Road traffic data collection : handling missing data / Abdelilah Mbarek, Mouna Jiber, Ali Yahyaouy, Abdelouahed Sabri -- Explainability of surrogate models for traffic signal control / Pawel Gora, Dominik Bogucki, M. Latif Bolum -- Intelligent techniques and explainable artificial intelligence for vessel traffic service : a survey / Meng Joo Er, Huibin Gong, Chuang Ma, Wenxiao Gao -- An explainable model for detection and recognition of traffic road signs / Anass Barodi, Abdelkarim Zemmouri, Abderrahim Bajita, Mohammed Benbrahim, Ahmed Tamtaouib -- An interpretable detection of transportation mode considering GPS, spatial, and contextual data based on ensemble machine learning / Sajjad Sowlati, Rahim Ali Abbaspour, Alireza Chehreghan -- Blockchain and explainable AI for trustworthy autonomous vehicles / Ouassima Markouh, Amina Adadi, Mohammed Berrada -- Ethical decision-making under different perspective-taking scenarios and demographic characteristics: the case of autonomous vehicles / Kareem Othman.
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 be hardly explained. This can be very problematic especially for systems of a safety-critical nature such as transportation systems. Explainable AI 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. Examining explainable AI in the field of ITS, this book has the following key features: provides the necessary background for newcomers to the field (both academics and interested partitioners). 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"-- Provided by publisher.
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2023018388
ISBN9781032344577 (hardback)
ISBN9781032348537 (paperback)
ISBN(ebook)

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