Enhancing safety feedback to the design of small, unmanned aircraft by joint assessment of impact area and human fatality

Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>A</mml:mi> <mml:mi>impact</mml:mi></mml:msub> <mml:mo>·</mml:mo> <mml:mi>P</mml:mi> <mml:mrow><mml:mo>{</mml:mo> <mml:mi>F</mml:mi> <mml:mo>|</mml:mo></mml:mrow> <mml:mrow><mml:mi>impact</mml:mi> <mml:mo>}</mml:mo></mml:mrow> </mml:mrow> <mml:annotation>${{A}_{{\mathrm{impact}}}} \cdot \mathbb{P}\{ F| {{\mathrm{impact}}\} } $</mml:annotation></mml:semantics> </mml:math> in case of a crashing UAS, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>A</mml:mi> <mml:mi>impact</mml:mi></mml:msub> <mml:annotation>${{A}_{{\mathrm{impact}}}}$</mml:annotation></mml:semantics> </mml:math> is the size of the crash impact area on the ground, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>P</mml:mi> <mml:mo>{</mml:mo> <mml:mi>F</mml:mi> <mml:mo>|</mml:mo> <mml:mrow><mml:mi>impact</mml:mi> <mml:mo>}</mml:mo></mml:mrow> </mml:mrow> <mml:annotation>$\mathbb{P}\{ F| {{\mathrm{impact}}\} } $</mml:annotation></mml:semantics> </mml:math> is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>A</mml:mi> <mml:mi>impact</mml:mi></mml:msub> <mml:mo>·</mml:mo> <mml:mi>P</mml:mi> <mml:mrow><mml:mo>{</mml:mo> <mml:mi>F</mml:mi> <mml:mo>|</mml:mo></mml:mrow> <mml:mrow><mml:mi>impact</mml:mi> <mml:mo>}</mml:mo></mml:mrow> <mml:mo>.</mml:mo></mml:mrow> <mml:annotation>${{A}_{{\mathrm{impact}}}} \cdot \mathbb{P}\{ F| {{\mathrm{impact}}\} } .$</mml:annotation></mml:semantics> </mml:math> In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>A</mml:mi> <mml:mi>impact</mml:mi></mml:msub> <mml:mo>·</mml:mo> <mml:mi>P</mml:mi> <mml:mrow><mml:mo>{</mml:mo> <mml:mi>F</mml:mi> <mml:mo>|</mml:mo></mml:mrow> <mml:mrow><mml:mi>impact</mml:mi> <mml:mo>}</mml:mo></mml:mrow> </mml:mrow> <mml:annotation>${{A}_{{\mathrm{impact}}}} \cdot \mathbb{P}\{ F| {{\mathrm{impact}}\} } $</mml:annotation></mml:semantics> </mml:math> . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.