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Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles

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Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 421))

Abstract

Automated navigation technology has established itself as an integral facet of intelligent transportation and smart city systems. Several international technological organizations have realized the immense potential of autonomous vehicular systems and are currently working towards their complete development for mainstream application. From deep learning algorithms for road object detection to intrusion detection systems for CAN bus monitoring, the functioning of a self-driving vehicle is powered by the simultaneous working of multiple inner vehicle module systems that perform proper vehicle navigation while ensuring the physical safety and digital privacy of the user. Transparency of the vehicle’s thought processes can assure the user of its credibility and reliability. This paper introduces explainable artificial intelligence, which aims to converge the decision-making processes of Autonomous Vehicle Systems (AVS). Here, the domain of Explainable AI (XAI) provides clear insights into the role of explainable AI in autonomous vehicles and increase human trust for AI based solutions in the same sector. This paper exhibits the trajectories of transportation advancements and the current scenario of the industry. A comparative quantitative and qualitative analysis is performed to compare the simulations of XAI and vehicular smart systems to showcase the significant developments achieved. Visual explanatory methods and an intrusion detection classifier were created as part of this research and achieved significant results over extant works.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India

    A. V. Shreyas Madhav & Amit Kumar Tyagi

  2. Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India

    Amit Kumar Tyagi

Authors
  1. A. V. Shreyas Madhav
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  2. Amit Kumar Tyagi
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Corresponding author

Correspondence to A. V. Shreyas Madhav .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KIET Group of Institutions, Ghaziabad, Delhi, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Science and Engineering, Nirma University, Ahmedabad, India

    Sudeep Tanwar

  4. Federal University of Piauí, Teresina, Brazil

    Joel J. P. C. Rodrigues

  5. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

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Madhav, A.V.S., Tyagi, A.K. (2023). Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_10

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  • DOI: https://doi.org/10.1007/978-981-19-1142-2_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1141-5

  • Online ISBN: 978-981-19-1142-2

  • eBook Packages: EngineeringEngineering (R0)

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