Automated Vehicles at Unsignalized Intersections: Safety and Efficiency Implications of Mixed Human and Automated Traffic

TL;DR

Understanding AV-HV interactions at unsignalized intersections is key for safety and efficiency. The authors analyze two large real-world AV datasets (Waymo Open and Lyft Level 5) to quantify merging and crossing conflicts using metrics including $TTC$, $PET$, $MRD$, and $TA$, and to classify conflicts as HV-HV, AV-HV, or HV-AV. They introduce MRD and the distribution of TA as novel measures, provide a meticulously processed conflict dataset for unsignalized intersections, and compare manufacturer-specific AV behaviors (Waymo vs Lyft) to reveal differences in safety margins and negotiation styles. The findings reveal a paradox where AVs maintain larger safety margins yet conservative behavior can create unsafe situations for human drivers, while cross-manufacturer differences underscore the need for standardization and human-centric AV behavior for safer, more efficient mixed-autonomy traffic.

Abstract

The integration of automated vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the interactions between AVs and human-driven vehicles (HVs) at intersections remains an open research question. This study aims to bridge this gap by examining behavioral differences and adaptations of AVs and HVs at unsignalized intersections by utilizing two large-scale AV datasets from Waymo and Lyft. By using a systematic methodology, the research identifies and analyzes merging and crossing conflicts by calculating key safety and efficiency metrics, including time to collision (TTC), post-encroachment time (PET), maximum required deceleration (MRD), time advantage (TA), and speed and acceleration profiles. Through this approach, the study assesses the safety and efficiency implications of these behavioral differences and adaptations for mixed-autonomy traffic. The findings reveal a paradox: while AVs maintain larger safety margins, their conservative behavior can lead to unexpected situations for human drivers, potentially causing unsafe conditions. From a performance point of view, human drivers tend to exhibit more consistent behavior when interacting with AVs versus other HVs, suggesting AVs may contribute to harmonizing traffic flow patterns. Moreover, notable differences were observed between Waymo and Lyft vehicles, which highlights the importance of considering manufacturer-specific AV behaviors in traffic modeling and management strategies for the safe integration of AVs. The processed dataset, as well as the developed algorithms and scripts, are openly published to foster research on AV-HV interactions.

Figures (12)

• Figure 1: The methodological framework employed in this study, illustrating the systematic process from data selection and preprocessing to conflict identification and behavioral analysis
• Figure 2: Visualization of Waymo and Lyft Level 5 vehicles
• Figure 3: Applying the low pass filter to speed profiles from Waymo and Lyft datasets
• Figure 4: Different steps for the automatic identification of unsignalized intersections with equal priority on all approaches from the raw datasets
• Figure 5: Examples of identified intersections, highlighting stop signs as indicators of unsignalized intersections with equal priority for all approaches.