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Assessing Dependability of Autonomous Vehicles

In: System Dependability and Analytics

Author

Listed:
  • Saurabh Jha

    (IBM T. J. Watson Research)

Abstract

Autonomous vehicles (AVs) such as self-driving cars and unmanned aerial vehicles are complex systems that use artificial intelligence (AI) and machine learning (ML) to make real-time navigational decisions. Ensuring the dependability of AVs in terms of robustness, correctness, reliability, and safety is critical for their mass deployment and public adoption. However, it is challenging to assess and ensure the dependability of these systems due to their complexity both in terms of software and hardware and in terms of the inherent stochasticity and uncertainty in the sensor data and ML/AI algorithms. In this chapter, we design and develop novel assessment techniques to rigorously validate the AV system, including its runtime operational characteristics. The developed assessment techniques address the challenges mentioned above and significantly outperform the current state-of-the-art assessment techniques. We demonstrate our developed techniques and scientific contributions using self-driving cars as a motivating example.

Suggested Citation

  • Saurabh Jha, 2023. "Assessing Dependability of Autonomous Vehicles," Springer Series in Reliability Engineering, in: Long Wang & Karthik Pattabiraman & Catello Di Martino & Arjun Athreya & Saurabh Bagchi (ed.), System Dependability and Analytics, pages 405-421, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-02063-6_24
    DOI: 10.1007/978-3-031-02063-6_24
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