Driving with Influence: Exploring Crash Factors of Automated Systems in Different Roadway Contexts University of Tennessee

Format:

Book

Conference/Event

Author/Creator:

Astle, W. Abram, author.

Haus, Samantha, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Level 2 (Partial driving automation).

Driving automation.

Risk management.

Automated vehicles.

Automated driving systems.

Local Subjects:

Level 2 (Partial driving automation).

Driving automation.

Risk management.

Automated vehicles.

Automated driving systems.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Although SAE Level 2 Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been shown to provide some safety benefits, they have largely been constrained to specific driving contexts, namely motorways for ADAS and lower speed roadways for ADS. As more advanced systems are entering the roadways and their operating conditions are expanding, it remains an ongoing challenge to assess the safe operation of vehicles with automation in different roadway contexts and leverage lessons learned from real-world incidents to create safer and more robust systems. As of August 2025, NHTSA's Standing General Order on Crash Reporting offers systematic data on such incidents, providing at least a cursory overview of where and how they occur. From this source, a total of 1,375 crash records were extracted, 657 for ADAS systems and 715 for ADS systems. Through the application of association rule mining and a novel metric termed influence, patterns in ADAS- and ADS-related crashes were examined within different roadway contexts. In general, it was found that subject vehicle and crash partner pre-crash movements as well as collision types were some of the most distinguishing factors between the two systems used. Differences in context specific rule summations also indicate distinct crash factor combinations between the two systems. The results offer an initial, exploratory perspective on the impact of vehicle automation on public roadways, providing insights that can inform system-specific safety assessments, risk mitigation strategies, and future research into the evolving dynamics of automated driving technologies

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license