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Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk

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Abstract

Road traffic accidents are largely driven by human error; therefore, the development of connected automated vehicles (CAV) is expected to significantly reduce accident risk. However, these changes are by no means proven and linear as different levels of automation show risk-related idiosyncrasies. A lack of empirical data aggravates the transparent evaluation of risk arising from CAVs with higher levels of automation capability. Nevertheless, it is likely that the risks associated with CAV will profoundly reshape the risk profile of the global motor insurance industry. This paper conducts a deep qualitative analysis of the impact of progressive vehicle automation and interconnectedness on the risks covered under motor third-party and comprehensive insurance policies. This analysis is enhanced by an assessment of potential emerging risks such as the risk of cyber-attacks. We find that, in particular, primary insurers focusing on private retail motor insurance face significant strategic risks to their business model. The results of this analysis are not only relevant for insurance but also from a regulatory perspective as we find a symbiotic relationship between an insurance-related assessment and a comprehensive evaluation of CAV’s inherent societal costs.

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Fig. 1

Source Illustration based on numbers provided by Destatis (2017) and Waymo (2017)

Fig. 2
Fig. 3

Source Own calculation based on data published by German Insurance Association (GDV 2016)

Fig. 4

Source calculations based on data published by German Insurance Association (GDV 2016)

Fig. 5

Source Own illustration based on numbers provided by Destatis (2017), Radke (2014) and Bundesanstalt für Straßenwesen (2017)

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Notes

  1. We use the number of collision-related insurance claims as a proxy for the total number of accidents including minor accidents. The number decreases to 3.36 accidents per million driven kilometres when selecting only police-recorded (Destatis 2017).

  2. The methodology defines the critical reason as the last failure in a causal chain. Therefore, it may not reflect the (only) cause of a crash and does not necessarily imply an assessment of fault. However, it does imply at least a contributory factor of human failure to an accident occurrence.

  3. This share of failure due to physical or mental shortcomings could be higher because the usage of smartphones has increasingly become a contributory reason for distraction within the last years and additional factors such as alcohol and drug abuse have not been considered in this source.

  4. Relevant critical situations for example could be driving too close to preceding vehicle (ACC), pedestrians/stationary object standing on the driving lane (AEB) and unintended departure from the driving lane (LKA).

  5. This factor is the result of the ratio of 111 reported disengagements of Waymo’s test fleet per million driven kilometres and 3.36 accidents per million driven kilometres in Germany in 2016.

  6. Premium figures are based on figures for year 2017 provided by GDV (2018).

  7. It is assumed that vehicles equipped with CAV technology especially in the beginning of market penetration will be relatively expensive due to required hardware (e.g. cameras and sensors) and software components.

  8. For instance, the anti-lock braking system (ABS) and electronic stability control (ESC) took about 20 and 15 years until more than 80% of all newly registered vehicles were equipped based on figures of the Deutsche Automobil Treuhand GmbH (DAT 2018). DAT 2018. DAT Report 2018.

  9. The average gross combined ratio of the motor insurance business between 2010 and 2016 in the German market is 102.2%. The combined ratio is the ratio of expenses for insurance operations and insurance claims to premiums.

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Funding

This work was supported by the VI-DAS (Vision Inspired Driver Assistance Systems), a European Commission Horizon 2020 research consortium [Grant Number 690772].

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Authors and Affiliations

  1. University of Limerick, Limerick, Ireland

    Fabian Pütz, Finbarr Murphy & Martin Mullins

  2. TH Köln University of Applied Sciences, Cologne, Germany

    Fabian Pütz

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  1. Fabian Pütz
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Correspondence to Finbarr Murphy.

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Pütz, F., Murphy, F. & Mullins, M. Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk. Environ Syst Decis 39, 383–395 (2019). https://doi.org/10.1007/s10669-019-09739-x

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