Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Sun, Zhanbo; Yao, Xue; Qin, Ziye; Peitong, Zhang; Yang, Zhijuan

doi:10.1177/03611981211020006

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…The dataset includes high-resolution lidar and camera data, recording vehicles’ type, size (i.e., length, width, and height), position (i.e., latitudinal and longitudinal), and movement (i.e., velocity). Note that the AVs do not have any communication with the infrastructure and there is no possibility for driver take-over or disengagement events ( 31 , 32 ). More information can be found at https://waymo.com/open/data/.…”

Section: Methodsmentioning

confidence: 99%

Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Wang

Farah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicles (AVs) are being introduced to the traffic system with the promise of improving current traffic status. However, the empirical data also indicate contrary effects with estimated higher crash rate and change of crash patterns. Therefore, it is necessary to investigate the driving behavior of AVs and human-driven vehicles (HDVs) in real mixed traffic. Current studies have analyzed the driving behavior of AVs and HDVs, as well as behavioral adaptations of drivers of HDVs based on empirical data. While they play an important role in traffic systems, signalized intersections have not been studied sufficiently in this context. Therefore, this study aims to utilize the Waymo open dataset to characterize and quantify the behavioral differences of AVs and HDVs at signalized intersections. Five parameters of driving behavior related to signalized intersections were characterized according to five critical maneuver phases, which were identified by wavelet transform and threshold-based method. Statistically significant differences in driving behavior between AVs and HDVs were found, from three categorized situations: vehicle approaching the red light/queue, vehicle responding to the green light (as the first vehicle), and vehicle responding to its preceding vehicle (in the queue). Further, behavioral adaptations of HDV drivers were revealed in that they tended to keep closer to the stopped AVs in a queue and to react more strongly to AV start-up maneuvers when the traffic light turns to green.

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Section: Methodsmentioning

confidence: 99%

Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Wang

Farah

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Another type of input variable is heterogeneity. Past researchers have stated that traffic flow and car-following combinations will affect car-following behavior [19,24,[34][35][36]. In this paper, different traffic flows are represented by the mean velocity of the lane.…”

Section: Input and Output Variablesmentioning

confidence: 99%

A Heterogeneity-Aware Car-Following Model: Based on the XGBoost Method

Zhu,

Yang,

Zhang

et al. 2024

Algorithms

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With the rising popularity of the Advanced Driver Assistance System (ADAS), there is an increasing demand for more human-like car-following performance. In this paper, we consider the role of heterogeneity in car-following behavior within car-following modeling. We incorporate car-following heterogeneity factors into the model features. We employ the eXtreme Gradient Boosting (XGBoost) method to build the car-following model. The results show that our model achieves optimal performance with a mean squared error of 0.002181, surpassing the model that disregards heterogeneity factors. Furthermore, utilizing model importance analysis, we determined that the cumulative importance score of heterogeneity factors in the model is 0.7262. The results demonstrate the significant impact of heterogeneity factors on car-following behavior prediction and highlight the importance of incorporating heterogeneity factors into car-following models.

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Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset

Wen

Cui

Jian

2022

Accident Analysis & Prevention

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Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Cited by 10 publications

References 44 publications

Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

A Heterogeneity-Aware Car-Following Model: Based on the XGBoost Method

Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset

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