Wang, Y.; Li, Y.; Li, R.; Wu, S.; Li, L. Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset. Appl. Sci.2024, 14, 2344.
Wang, Y.; Li, Y.; Li, R.; Wu, S.; Li, L. Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset. Appl. Sci. 2024, 14, 2344.
Wang, Y.; Li, Y.; Li, R.; Wu, S.; Li, L. Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset. Appl. Sci.2024, 14, 2344.
Wang, Y.; Li, Y.; Li, R.; Wu, S.; Li, L. Unraveling Spatial–Temporal Patterns and Heterogeneity of On-Ramp Vehicle Merging Behavior: Evidence from the exiD Dataset. Appl. Sci. 2024, 14, 2344.
Abstract
Understanding the spatiotemporal characteristics of merging behavior is crucial for the ad-vancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merg-ing patterns, and investigate how various factors, such as merging scenarios and vehicle types, in-fluence driving behavior. Initially, a framework based on High-Definition (HD) Map is devel-oped to extract trajectory information in a meticulous manner. Subsequently, eight distinct merg-ing patterns (Pattern A to H) are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane's spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifying high-risk situations. Our novel findings reveal that Patterns C and F, with Time-to-Collision (TTC) values less than 2.5 seconds, pose a significantly higher risk than other patterns. In traffic simulations, Patterns B and E can be integrated, as they show negligible differences in speed, distance traveled, and duration. Additionally, the practice of truck platooning has a signif-icant impact on vehicle merging behavior. This study enhances the understanding of merging be-havior, facilitating autonomous vehicles' ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic manage-ment, and enabling the effective integration of autonomous driving systems with human drivers.
Keywords
Merging patterns; Driving heterogeneity; HD map
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
