Estimated benefit of automated emergency braking systems for vehicle–pedestrian crashes in the United States

Figures & data

Table 1. Comparison of pedestrian effectiveness studies conducted in the United States and internationally.

Data source	AEB system	Target pedestrian population	Estimated effectiveness	Source
FARS and GES (2005–2009)	Ideal	All single-vehicle pedestrian crashes	Mitigate up to 65% of pedestrian collisions and 58% of pedestrian deaths in single-vehicle crashes in the United States	Jermakian and Zuby (2011)
FARS and GES (2011–2012), test track data on pedestrian AEB system detection ability	Account for AEB system detection ability	All car and LTV frontal crashes with no driver avoidance maneuvers in specific crash scenarios	Avoid 77.6% of crashes in target population	Yanagisawa et al. (2017)
Great Britain data STATS19 (2008–2010) and OTS (2000–2010), German data GIDAS 2014	Account for current systems and future ideal systems	All car frontal crashes	Great Britain: £119–£385 million annually Germany: €63–€216 million annually	Edwards et al. (2014)
CASR (Australia) and IFSTTAR-LMA (France) crash databases	Camera with 35° field of view	Sample of 100 car–pedestrian crashes	90% detection rate and 75% avoidance rate for reconstructed cases	Hamdane et al. (2015)
PCDS (U.S.) crash database	Account for range of present and future system parameters	All car and LTV frontal crashes with pedestrians age 15 and up	Avoid 34–86% of crashes Mitigate 36–87% of fatalities	This study

GIDAS: German In-Depth Accident Study; OTS: On the Spot; CASR: Center for Automotive Safety Research; IFSTTAR-LMA: French Institute of Science and Technology for Transport development and networks - Laboratory of Accident Mechanism Analysis.

Figure 1. AEB system effectiveness at a range of TTC values and latency values for both the late/hard braking driver scenario and the early/weak braking driver scenario.

Figure 2. AEB system average change in velocity for unavoided cases for a range of TTC and latency values for both the late/hard braking driver scenario and the early/weak braking driver scenario. The number on the bar corresponds to the number of unavoided cases.

Figure 3. Percentage change in fatality risk for a range of TTC and latency values for both the late/hard braking driver scenario and the early/weak braking driver scenario.

Figure 4. Percentage change in MAIS 3 + F risk for a range of TTC and latency values for both the late/hard braking driver scenario and the early/weak braking driver scenario.

Jermakian JS, Zuby DS. Primary Pedestrian Crash Scenarios: Factors Relevant to the Design of Pedestrian Detection Systems. Arlington, VA: Insurance Institute for Highway Safety; 2011.

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Yanagisawa M, Swanson ED, Azeredo P, Najm W. Estimation of Potential Safety Benefits for Pedestrian Crash Avoidance/Mitigation Systems. Washington, DC: NHTSA; 2017. DOT HS 812 400.

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Edwards M, Nathanson A, Wisch M. Estimate of potential benefit for Europe of fitting autonomous emergency braking (AEB) systems for pedestrian protection to passenger cars. Traffic Inj Prev. 2014;15(Suppl. 1):S173–S182.

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Hamdane H, Serre T, Masson C, Anderson R. Issues and challenges for pedestrian active safety systems based on real world accidents. Accid Anal Prev. 2015;82:53–60.

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Data availability statement

The original data sources for this study, PCDS, GES, and FARS, are publicly available from NHTSA. The reconstructions performed for this study are not publicly available.