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Control and Safety of Autonomous Vehicles with Learning-Enabled Components

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Safe, Autonomous and Intelligent Vehicles

Part of the book series: Unmanned System Technologies ((UST))

Abstract

Real-world autonomous systems, such as autonomous vehicles, often operate in uncertain and partially observable environments. In such scenarios, designing a controller that achieves the desired behavior on the system is a challenging problem. The proven efficacy of learning-based control schemes strongly motivates their application to autonomous vehicles. However, guaranteeing correct operation of learning-based schemes during and after the learning process is currently an unresolved issue, which is of vital importance in safety-critical systems such as autonomous vehicles. Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the availability of well-developed numerical tools.

In this chapter, we provide a brief overview of the challenges associated with system verification when learning is involved in the control loop, some recent attempts to address these challenges based on HJ reachability, and the open questions that remain to be answered.

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Notes

  1. 1.

    A function f : X → Y  between two measurable spaces (X, ΣX) and (Y, ΣY) is said to be measurable if the preimage of a measurable set in Y  is a measurable set in X, that is: ∀V ∈ ΣY, f −1(V ) ∈ ΣX, with ΣX, ΣY σ-algebras on X,Y .

  2. 2.

    We will omit the notation (s) from variables such as x and a when referring to function values here on.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

    Somil Bansal & Claire J. Tomlin

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  1. Somil Bansal
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  2. Claire J. Tomlin
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Corresponding author

Correspondence to Claire J. Tomlin .

Editor information

Editors and Affiliations

  1. Boeing Research and Technology, Huntsville, AL, USA

    Huafeng Yu

  2. Duke University, Durham, NC, USA

    Xin Li

  3. California Institute of Technology, Pasadena, CA, USA

    Richard M. Murray

  4. General Motors R&D, Warren, MI, USA

    S. Ramesh

  5. University of California, Berkeley, CA, USA

    Claire J. Tomlin

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Bansal, S., Tomlin, C.J. (2019). Control and Safety of Autonomous Vehicles with Learning-Enabled Components. In: Yu, H., Li, X., Murray, R., Ramesh, S., Tomlin, C. (eds) Safe, Autonomous and Intelligent Vehicles. Unmanned System Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-97301-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-97301-2_4

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