Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments

Li, Tienan; Chen, Danjue; Zhou, Hao; Laval, Jorge A.; Xie, Yuanchang

doi:10.1016/j.trb.2021.03.003

Cited by 77 publications

(48 citation statements)

References 27 publications

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“…3 suggest the slow controller needs longer time. Both findings are consistent with our interpretation of the mathematical derivations (17) and (20).…”

Section: A Actuator Performancesupporting

confidence: 93%

“…If the planner is more sensitive to spacing change (i.e., with a larger k v ), the ACC system is more likely to be string unstable (i.e., larger ∆v target will make the planned trajectory harder to track). For the impact of time headway H t , (17) indicates larger headway can benefit the SS, which is consistent with the recent empirical finding [20] from a few commercial ACC systems. Remarkably, the theoretic analysis of string stability in frequency domain also achieves the similar finding but in a different way, i.e.…”

Section: A Pi Low-level Controller and Its Impact On Sssupporting

confidence: 88%

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Significance of Low-level Controller for String Stability under Adaptive Cruise Control

Zhou¹,

Zhou²,

Li³

et al. 2021

Preprint

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Current factory adaptive cruise control (ACC) systems consist of an upper-level planner that computes the optimal trajectory of the vehicle and a low-level controller in charge of executing it. The literature on the string stability (SS) of ACC systems is mostly concerned with the upper-level planner. In this paper, we show that the low-level controller can have a significant impact on SS. We find that i) a fast controller improves the SS, ii) a slow controller can result from either insufficient control gains or from an undershooting gas/brake system or both, and iii) although the integral gain is helpful for effective control, the integral windup can cause overshooting, and in turn undermines the SS of the ACC. Accordingly, we suggest tuning up the proportional/feedforward gain and ensuring the gas/brake is not to undershoot. Anti-windup design is also needed to improve SS. The results of this paper are validated both numerically and empirically on commercial cars.

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“…3 suggest the slow controller needs longer time. Both findings are consistent with our interpretation of the mathematical derivations (17) and (20).…”

Section: A Actuator Performancesupporting

confidence: 93%

Section: A Pi Low-level Controller and Its Impact On Sssupporting

confidence: 88%

Significance of Low-level Controller for String Stability under Adaptive Cruise Control

Zhou¹,

Zhou²,

Li³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has been more than 20 years since the advent of the first vehicle equipped with ACC technology [15]. Moreover, the ACC technology is likely to be the primary component for longitudinal control of higher-level AVs in the future [16]. erefore, it is critical to understand the changes brought about by vehicles equipped with ACC systems compared with traditional HDVs.…”

Section: Introductionmentioning

confidence: 99%

An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

Ding

Chen

et al. 2021

Journal of Advanced Transportation

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The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.

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“…However, in-depth analysis of the ACC behaviors based on this valuable dataset is not yet available. Recently, we have conducted extensive field experiments using four different commercial ACC car models (referred to as the MA Experiments) and investigated their CF behaviors under disturbances Li et al (2021). It was found that ACC behaviors were complex and they largely depend on headway settings, speed levels, and the features of stimulus from the lead vehicle.…”

Section: Introductionmentioning

confidence: 99%

Fundamental Diagrams of Commercial Adaptive Cruise Control: Worldwide Experimental Evidence

Li¹,

Chen²,

Zhang³

et al. 2021

Preprint

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Experimental measurements on commercial adaptive cruise control (ACC) vehicles is becoming increasingly available from around the world, providing an unprecedented opportunity to study the traffic flow characteristics that arise from this technology. This paper adds new experimental evidence to this knowledge base and presents a comprehensive empirical study on the ACC equilibrium behaviors via the resulting fundamental diagrams. We find that like human-driven vehicles, ACC systems display a linear speed-spacing relationship (within the range of available data) but the key parameters of these relationships can differ significantly from human-driven traffic depending on input settings: At the minimum headway setting, capacities in excess of 3500 vehicles per hour are observed, together with an extremely fast congested wave speed of 50 miles per hour on average. These fast waves are unfamiliar to human drivers, and may or may not pose a safety risk. We also find that ACC jam spacing is much larger than in human traffic, which reduces the network storage capacity. Our findings suggest that future research directions should include ACC in very low speed and complete stop conditions and also the responses of human-drivers to ACC.

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Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments

Cited by 77 publications

References 27 publications

Significance of Low-level Controller for String Stability under Adaptive Cruise Control

Significance of Low-level Controller for String Stability under Adaptive Cruise Control

An Extended Car-Following Model in Connected and Autonomous Vehicle Environment: Perspective from the Cooperation between Drivers

Fundamental Diagrams of Commercial Adaptive Cruise Control: Worldwide Experimental Evidence

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