Simulation of Automated Vehicles' Drive Cycles

LeVine, Scott · 2018 · ROSA P / City University of New York. University Transportation Research Center

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Summary

This research addresses the impact of automated vehicles (AVs) on freeway capacity, specifically examining how legal standards of care influence driving behavior and traffic flow. The study is motivated by the need to understand how AVs, which must adhere to the "Assured Clear Distance Ahead" (ACDA) doctrine—a legal requirement to stop within visible distance—will perform compared to human drivers who often engage in "educated risk-taking" by maintaining shorter following distances. The authors aim to characterize the system-level capacity impacts of ACDA-compliant driving strategies, quantifying the trade-off between safety and throughput while accounting for kinematic uncertainty. The methodology involves developing a straightforward ACDA-compliant automated-driving model to analytically estimate freeway "pipeline" capacity. The analysis focuses on mainline freeway segments with homogeneous AV traffic, excluding connectivity (V2X) and mixed human-AV traffic. Kinematic parameters are derived from empirical field tests, and the model explicitly accounts for uncertainty in braking-system performance using empirical distributions. The study compares ACDA-compliant AV behavior against human driving models, such as the Highway Capacity Manual (HCM-2010) and Wiedemann’s car-following models, to generate fundamental diagrams (relationships between speed and flow). The analysis also evaluates the vulnerability of AVs to lateral cut-ins and quantifies capacity trade-offs across various degrees of safety, ranging from one failure in 100,000 events to one in 1,000,000. The findings indicate that AVs pursuing ACDA-compliant strategies exhibit distinctive fundamental diagrams. Under baseline assumptions, these vehicles sustain higher flow rates at free-flow speeds than human drivers but experience a steeper degradation in speed as traffic volume increases. While ACDA-compliant AVs achieve a higher maximum-achievable throughput, the specific impact depends on the interpretation of ACDA; the "strong" interpretation, which requires avoiding stationary objects obscured by leading vehicles, significantly reduces roadway capacity. The study demonstrates that faithfully replicating human driving behavior on congested freeways could expose manufacturers to unacceptable liability risks, whereas reducing this risk through conservative driving unavoidably lowers network capacity. Additionally, the research highlights that the primary mechanism affecting turning movement throughput is speed, constrained by passenger comfort, with AVs potentially increasing turning traffic throughput by 0.2% to 43%. The significance of this work lies in its novel quantification of the trade-off between freeway capacity and safety standards for automated vehicles. It reveals that legal liability concerns may force AV manufacturers to adopt more conservative driving behaviors than human drivers, leading to distinct traffic flow characteristics. This has implications for transportation planning and policy, suggesting that the integration of AVs may not simply replicate human traffic patterns but could alter fundamental capacity metrics. The study underscores the need for further research into mixed traffic conditions, heavy vehicles, and the effects of sensor uncertainty, providing a theoretical and empirical foundation for understanding the systemic impacts of vehicle automation.

Key finding

ACDA-compliant automated vehicles sustain higher flow rates at free-flow speeds than human drivers but exhibit steeper speed degradation under congestion, while potentially increasing turning traffic throughput by up to 43%.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density
• situational awareness
• driverless ads
• following distance
• automation surprise
• passenger motion sickness comfort

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Methodological Resource: dataset resource, tool software
• Theoretical Contribution: computational model