0-7033: Defining Operational Design Domains (ODDs) for the Safe Blending of Levels 0-4 Connected and Autonomous Vehicles (CAVs) in the Traffic Streams

Chin, Kristie; Wang, Junmin; Zhou, Xingyu; Ross, Heidi; Gold, Andrea; Sample, Mikhaela · 2023 · ROSA P / Texas Department of Transportation. Research and Technology Implementation Office

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Summary

This study addresses the lack of consensus regarding the evaluation of Automated Vehicle (AV) safety, specifically concerning Operational Design Domains (ODDs). As Texas advances AV deployments, the Texas Department of Transportation (TxDOT) requires a standardized framework to ensure public safety and facilitate dialogue between public and private sectors. The research, conducted by the University of Texas at Austin Center for Transportation Research (CTR) and the Mobility Systems Lab, aimed to define an ODD framework for Levels 0–4 Connected and Autonomous Vehicles (CAVs), evaluate AV performance through simulation, and formulate policy and technical recommendations for Texas. The researchers first conducted a literature review and engaged stakeholders to identify roadway environments relevant to early deployments and ten problematic environments requiring special consideration. They selected five specific scenarios for testing, creating virtual highway and urban environments to compare automated driving systems (ADS) against human driving behavior. The experimental design involved recruiting 27 participants to test these scenarios in both automated and manual modes, focusing on weaving and forced merge situations. The team analyzed safety, mobility, and human-factors results using a developed performance metric framework. Additionally, they tested a prototype takeover alert system to measure driver reaction times when resuming control. The findings demonstrated that the ADS was safer than human participants across all performance metrics. In terms of time-to-collision (TTC), human drivers exhibited more aggressive behavior with lower TTC values, whereas the Adaptive Cruise Control (ACC) feature successfully increased TTC and safety. Regarding headways, both humans and ADS reduced following distances as traffic flow increased from Level of Service (LOS) A to D; however, the ACC feature maintained more consistent following distances, thereby enhancing occupant safety. Lateral offset analysis revealed that human drivers deviated an average of 0.4 meters from the lane centerline, frequently crossing boundaries, while the ADS maintained an average offset of only 0.04 meters, staying consistently centered. Furthermore, the ADS optimized traffic flow by producing significantly smoother velocity profiles compared to human drivers, attributed to advanced sensing and control algorithms. In takeover tests, participants regained control in under 2.0 seconds, though the study noted a need for further research into automation complacency risks. The significance of this research lies in its contribution to future legislation and infrastructure readiness in Texas. By establishing a common ODD framework and providing evidence-based technical recommendations, the study supports TxDOT in guiding the Texas legislature on terminology, policy best practices, and safety standards. The results suggest that AVs have the potential to reduce congestion and emissions while improving safety, reinforcing Texas’s position as a leader in AV deployment through a business-friendly regulatory environment.

Key finding

The automated driving system was safer than human participants across all performance metrics, including time-to-collision, headway maintenance, and lateral lane centering.

Methodology

simulator

Sample size: 27

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• driverless ads
• odd communication
• situational awareness

Information type

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• Methodological Resource: dataset resource, tool software
• Theoretical Contribution: conceptual framework