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Measuring Automated Vehicle Safety

  • Conference paper
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Road Vehicle Automation 11 (ARTSymposium 2023)

Part of the book series: Lecture Notes in Mobility ((LNMOB))

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Abstract

Measuring the safety of the operation and behavior of automated vehicles in traffic requires one or more leading indicators, based on non-collision interactions, to produce a quantitative score reflecting the riskiness or safeness of the behavior of vehicles in traffic. Such measurements are critical to the deployment and public acceptance of automated mobility systems.

A collision hazard measure with the essential characteristics [1] to provide an effective measurement of safety that will be useful to AV developers, traffic infrastructure developers and managers, regulators, and the public is presented here. This collision hazard measure (SHM) is a kinematic measure that overcomes the limitations of existing measures, and provides an independent leading quantitative measurement of safety.

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Notes

  1. 1.

    In a histogram of this type, the range of hazard values (0 to 100) is divided into a number of distinct bins. For a given data set, the number of hazard values that are within each bin are counted, and displayed as a bar-chart.

  2. 2.

    Prof. J. Christian Gerdes of Stanford University has spoken recently at Transportation Research Board (TRB) meetings about establishing a “reasonable” level of acceleration for autonomous vehicles. His “reasonable” acceleration is the same concept as the “safe” level of acceleration used in the measure here.

  3. 3.

    \(n(n-1)/2\).

  4. 4.

    The mathematical symbol m is used here for SHM with sequentially numbered subscripts denoting successive refinements of the measure.

  5. 5.

    AVs utilize sensors and computers to sense and perceive the traffic situation, and to decide what actions to take in response.

  6. 6.

    Road/street traction or grip can be estimated from video imagery, or measured independently periodically, or determined from an on-board traction-control system. grip also has the units of \(\left[ \textrm{length}/\textrm{time}^{2} \right] \)  or \(\left[ \textrm{acceleration} \right] \).

  7. 7.

    The three arcs on each side represent the sharpest possible turn radius for the Ego vehicle at the slowest, fastest, and median speed of the Ego vehicle during the duration of this track.

  8. 8.

    Data from police records.

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Thanks to Ravi Patel who contributed to the example data presented here, and Andy Postman who provided helpful suggestions that improved the text.

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Authors and Affiliations

  1. Streetscope, Inc., 130 W. Union Street, Pasadena, CA, 91103, USA

    Erik K. Antonsson

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  1. Erik K. Antonsson
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Correspondence to Erik K. Antonsson .

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Editors and Affiliations

  1. VDI/VDE Innovation + Technik GmbH, Berlin, Berlin, Germany

    Gereon Meyer

  2. Stanford University, Palo Alto, CA, USA

    Sven Beiker

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Antonsson, E.K. (2024). Measuring Automated Vehicle Safety. In: Meyer, G., Beiker, S. (eds) Road Vehicle Automation 11. ARTSymposium 2023. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-031-67466-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-67466-2_13

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