One of the First Fatalities of a Self-Driving Car: Root Cause Analysis of the 2016 Tesla Model S 70D Crash

Ergin, Uluğhan · 2022 · Crossref

DOI: 10.38002/tuad.1084567

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Summary

**Summary** This case study investigates the root causes of a fatal 2016 crash involving a Tesla Model S 70D operating in autopilot mode and a Freightliner semi-trailer. The research addresses the growing safety concerns surrounding SAE Level 2 automated vehicles, specifically the risk of driver disengagement and over-reliance on Advanced Driver Assistance Systems (ADAS). The study aims to determine why the Tesla driver failed to take evasive action and why safety-critical ADAS features did not prevent the collision. The analysis utilizes data from the National Highway Traffic Safety Administration’s Special Crash Investigation (SCI) Report. The crash occurred when the Tesla, traveling at 119 km/h with Traffic-Aware Cruise Control engaged, failed to brake or steer as a truck turned left across its path. The driver, who had approximately 7.25 seconds of clear visibility before impact, did not react, resulting in the Tesla underriding the trailer and the driver’s immediate death. The study applies two specific root cause analysis tools: the Five Whys Technique to examine human error and Barrier Analysis to evaluate equipment failure. The Five Whys analysis identifies the primary human factor as a lack of situational awareness driven by cognitive underload. The study argues that the monotonous nature of monitoring automated systems leads to disengagement, causing drivers to over-rely on ADAS and fail to perceive hazards. The Barrier Analysis reveals that while the ADAS systems (Forward Collision Warning and Automatic Emergency Braking) were functional, they failed to trigger due to the specific cross-path configuration of the vehicles and road characteristics. The analysis concludes that these safety barriers are not infallible and can be bypassed by certain geometric scenarios. The findings highlight that human error, specifically the failure to maintain vigilance during automated driving, was the central cause of the fatality, compounded by the limitations of ADAS in complex intersection scenarios. The paper emphasizes that until fully autonomous vehicles are prevalent, drivers must maintain active situational awareness. It recommends that manufacturers and regulators address the "cognitive underload" risk by improving driver engagement strategies and clearly communicating the limitations of ADAS to prevent over-reliance. This work contributes to the field by providing a structured framework for analyzing fatalities in transitional automated vehicle technologies, underscoring that safety-critical systems require continuous human oversight.

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Topics

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• pre crash contributing factors
• situational awareness
• automation surprise
• naturalistic crash near crash

Information type

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• Empirical Findings: crash risk outcomes