https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0730-4405
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Understanding individual car drivers’ behavioural variations and heterogeneity is a significant aspect of developingcar simulator technologies, which are widely used in transport safety. This also characterizes the heterogeneity in drivers’ behaviour in terms of risk and hurry, using both real-time on-track and in-simulator driving performance features. Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain drivers’ behaviour while being classified and the explanations for them are evaluated. However, the high predictive power of ML algorithms ignore the characteristics of non-stationary domain relationships in spatiotemporal data (e.g., dependence, heterogeneity), which can lead to incorrect interpretations and poor management decisions. This study addresses this critical issue of ‘interpretability’ in ML-based modelling of structural relationships between the events and corresponding features of the car drivers’ behavioural variations. In this work, an exploratory experiment is described that contains simulator and real driving concurrently with a goal to enhance the simulator technologies. Here, initially, with heterogeneous data, several analytic techniques for simulator bias in drivers’ behaviour have been explored. Afterwards, five different ML classifier models were developed to classify risk and hurry in drivers’ behaviour in real and simulator driving. Furthermore, two different feature attribution-based explanation models were developed to explain the decision from the classifiers. According to the results and observation, among the classifiers, Gradient Boosted Decision Trees performed best with a classification accuracy of 98.62%. After quantitative evaluation, among the feature attribution methods, the explanation from Shapley Additive Explanations (SHAP) was found to be more accurate. The use of different metrics for evaluating explanation methods and their outcome lay the path toward further research in enhancing the feature attribution methods.

Place, publisher, year, edition, pages
2023.
Series
International Conference on Agents and Artificial Intelligence, ISSN 21843589
Keywords [en]
Artificial Intelligence, Driving Behaviour, Feature Attribution, Evaluation, Explainable Artificial Intelligence, Interpretability, Road Safety.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64912DOI: 10.5220/0011801000003393Scopus ID: 2-s2.0-85182555765OAI: oai:DiVA.org:mdh-64912DiVA, id: diva2:1816110
Conference
15th International Conference on Agents and Artificial Intelligence (ICAART 2023)
Available from: 2023-11-30 Created: 2023-11-30 Last updated: 2024-12-04Bibliographically approved
In thesis
1. Explainable Artificial Intelligence for Enhancing Transparency in Decision Support Systems
Open this publication in new window or tab >>Explainable Artificial Intelligence for Enhancing Transparency in Decision Support Systems
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Artificial Intelligence (AI) is recognized as advanced technology that assist in decision-making processes with high accuracy and precision. However, many AI models are generally appraised as black boxes due to their reliance on complex inference mechanisms.  The intricacies of how and why these AI models reach a decision are often not comprehensible to human users, resulting in concerns about the acceptability of their decisions. Previous studies have shown that the lack of associated explanation in a human-understandable form makes the decisions unacceptable to end-users. Here, the research domain of Explainable AI (XAI) provides a wide range of methods with the common theme of investigating how AI models reach to a decision or explain it. These explanation methods aim to enhance transparency in Decision Support Systems (DSS), particularly crucial in safety-critical domains like Road Safety (RS) and Air Traffic Flow Management (ATFM). Despite ongoing developments, DSSs are still in the evolving phase for safety-critical applications. Improved transparency, facilitated by XAI, emerges as a key enabler for making these systems operationally viable in real-world applications, addressing acceptability and trust issues. Besides, certification authorities are less likely to approve the systems for general use following the current mandate of Right to Explanation from the European Commission and similar directives from organisations across the world. This urge to permeate the prevailing systems with explanations paves the way for research studies on XAI concentric to DSSs.

To this end, this thesis work primarily developed explainable models for the application domains of RS and ATFM. Particularly, explainable models are developed for assessing drivers' in-vehicle mental workload and driving behaviour through classification and regression tasks. In addition, a novel method is proposed for generating a hybrid feature set from vehicular and electroencephalography (EEG) signals using mutual information (MI). The use of this feature set is successfully demonstrated to reduce the efforts required for complex computations of EEG feature extraction.  The concept of MI was further utilized in generating human-understandable explanations of mental workload classification. For the domain of ATFM, an explainable model for flight take-off time delay prediction from historical flight data is developed and presented in this thesis. The gained insights through the development and evaluation of the explainable applications for the two domains underscore the need for further research on the advancement of XAI methods.

In this doctoral research, the explainable applications for the DSSs are developed with the additive feature attribution (AFA) methods, a class of XAI methods that are popular in current XAI research. Nevertheless, there are several sources from the literature that assert that feature attribution methods often yield inconsistent results that need plausible evaluation. However, the existing body of literature on evaluation techniques is still immature offering numerous suggested approaches without a standardized consensus on their optimal application in various scenarios. To address this issue, comprehensive evaluation criteria are also developed for AFA methods as the literature on XAI suggests. The proposed evaluation process considers the underlying characteristics of the data and utilizes the additive form of Case-based Reasoning, namely AddCBR. The AddCBR is proposed in this thesis and is demonstrated to complement the evaluation process as the baseline to compare the feature attributions produced by the AFA methods. Apart from generating an explanation with feature attribution, this thesis work also proposes the iXGB-interpretable XGBoost. iXGB generates decision rules and counterfactuals to support the output of an XGBoost model thus improving its interpretability. From the functional evaluation, iXGB demonstrates the potential to be used for interpreting arbitrary tree-ensemble methods.

In essence, this doctoral thesis initially contributes to the development of ideally evaluated explainable models tailored for two distinct safety-critical domains. The aim is to augment transparency within the corresponding DSSs. Additionally, the thesis introduces novel methods for generating more comprehensible explanations in different forms, surpassing existing approaches. It also showcases a robust evaluation approach for XAI methods.

Place, publisher, year, edition, pages
Västerås: Mälardalen university, 2024
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 397
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-64909 (URN)978-91-7485-626-2 (ISBN)
Public defence
2024-01-30, Gamma, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2023-12-04 Created: 2023-12-01 Last updated: 2024-01-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Islam, Mir RiyanulAhmed, Mobyen UddinBegum, Shahina

Search in DiVA

By author/editor
Islam, Mir RiyanulAhmed, Mobyen UddinBegum, Shahina
By organisation
Embedded Systems
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 85 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf