To Err is Human: The role of human safety metrics in an age of Automated Vehicles Intel Corporation

Format:

Book

Conference/Event

Author/Creator:

Weast, Jack, author.

Contributor:

Alvarez, Ignacio

Elli, Maria

Kovesdy, Scott

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

To Err is Human: The role of human safety metrics in an age of Automated VehiclesAs industry races to complete technical development of automated driving systems (ADS)-equipped vehicles (AVs), important questions are being raised about how to measure the safety of such systems. Traffic safety engineers have for decades utilized metrics to assess the safety of human drivers and measurements such as Time To Collision (TTC) and Time Headway (THW) have proven to be an indicator of risk of an accident for human drivers.But what if we can do better with AVs? Are human derived risk metrics meaningful for a self-driving vehicle? Recently, the Institute for Automated Mobility (IAM) published a set of new metrics defined specifically for self-driving vehicles that provide a thorough assessment of the safety of an AV. While humans must use judgement to make decisions, AVs can use precise measurement techniques via sensors and correlate location data in real time. Utilizing information such as the reaction time of the ADS, the braking capability of the AV, and more, the IAM proposed metrics allow for the assessment of the safety of an AV to be accurately measured, not as a notion of variable risk, but as a binary assessment of safety.In this paper we analyze, compare and contrast human risk-oriented safety metrics with the more definitive metrics proposed for AV's. We answer important questions about the necessary evolution of human derived metrics to ensure they are meaningful in the assessment of the safety of an AV, as well as whether novel new metrics proposed for AV's can be used to better understand and assess the safety performance of AVs and human drivers. Our research proves that AV-based assessment metrics can provide better insight into the safety of both AVs and human drivers

Notes:

Vendor supplied data

Publisher Number:

2021-01-0875

Access Restriction:

Restricted for use by site license