Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics

In most studies on modeling driving behavior at uncontrolled intersections, multi-vehicle interaction scenarios are usually categorized and modeled separately as moving-across behavior and merging behavior. However, it is inappropriate to use a single-behavior model to accurately represent general driving behavior in uncontrolled intersections. In this case, we constructed a general driving behavior model for multi-vehicle interaction at uncontrolled intersections. Initially, the IMM model is employed to anticipate the movement of the vehicle within the driver’s visual field. The risk field theory is applied to assess potential hazards that the vehicle might confront, drawing from the risk homeostasis theory and preview-follower theory, which aids in determining a trajectory that aligns with the drivers’ real-life actions while also meeting the risk constraints. Drivers’ heterogeneity is reflected by risk threshold. This model can simulate driver behavior in traffic congestion at uncontrolled intersections by adjusting risk thresholds when the vehicles are caught in a deadlock situation. Results show that our model can accurately reproduce the priority and trajectory of vehicles crossing the intersection and resolve multi-vehicle conflicts within a reasonable time. This model can be used for traffic simulation at uncontrolled intersections and to provide test validation for automated driving systems.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-10-03
DOI
https://doi.org/10.1109/tits.2024.3465442
Citations
13
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
3.45

Funders

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article

Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics

Haitian Tan, Guangquan Lu, Zhaojie Wang
13 citations
IEEE Transactions on Intelligent Transportation Systems
Traffic Prediction and Management Techniques
3.45
article

Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics

Haitian Tan, Guangquan Lu, Zhaojie Wang
article en
13 citations

Abstract

In most studies on modeling driving behavior at uncontrolled intersections, multi-vehicle interaction scenarios are usually categorized and modeled separately as moving-across behavior and merging behavior. However, it is inappropriate to use a single-behavior model to accurately represent general driving behavior in uncontrolled intersections. In this case, we constructed a general driving behavior model for multi-vehicle interaction at uncontrolled intersections. Initially, the IMM model is employed to anticipate the movement of the vehicle within the driver’s visual field. The risk field theory is applied to assess potential hazards that the vehicle might confront, drawing from the risk homeostasis theory and preview-follower theory, which aids in determining a trajectory that aligns with the drivers’ real-life actions while also meeting the risk constraints. Drivers’ heterogeneity is reflected by risk threshold. This model can simulate driver behavior in traffic congestion at uncontrolled intersections by adjusting risk thresholds when the vehicles are caught in a deadlock situation. Results show that our model can accurately reproduce the priority and trajectory of vehicles crossing the intersection and resolve multi-vehicle conflicts within a reasonable time. This model can be used for traffic simulation at uncontrolled intersections and to provide test validation for automated driving systems.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(11)
Beihang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Traffic Prediction and Management Techniques
3.45
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Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers’ Visual Field Characteristics — Haitian Tan, Guangquan Lu, et al. · IEEE Transactions on Intelligent Transportation Systems (2024) | TGRS Research Map | TGRS