Congestion-Aware Cooperative Adaptive Cruise Control for Mitigation of Self-Organized Traffic Jams

Kim, Taehooie; Jerath, Kshitij

doi:10.1109/tits.2021.3059237

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…Va īi = c 4 (x ei cos ψei + c 3 ȳei sin ψei ) (34) to provide a similar stability result as Theorem 2, with distributed (instead of centralized) communication. The difference between ( 25)-( 27) and ( 32)-( 34) is replacing x ei , y ei , ψ ei (formation error with the leader) with xei , ȳei , ψei (formation error with the neighbor), and replacing ψ c0 − ψ 0 (signal from leader) with ψ c ī − ψ¯i (signal from neighbor).…”

Section: Distributed Adaptationmentioning

confidence: 81%

“…58, NO. 1 FEBRUARY 2022 unmanned vehicles has been overlooked for a long time in the literature, but it has attracted some attention recently: for example, for connected ground vehicles it has been shown that there are tradeoffs between the density of vehicles and the interference caused by many vehicles communicating with each other: these tradeoffs require congestion-aware formation control methods [34], [35].…”

Section: Introductionmentioning

confidence: 99%

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Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

Baldi

Sun

Zhou

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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This article presents a new adaptive method for formation control of unmanned aerial vehicles (UAVs) with limited leader information and communication. We study a formation control protocol in the framework of vector-field guidance where the leader can communicate its position and orientation but not its velocity. A practical motivation for this scenario is the so-called congestion-aware control, in which tradeoffs between the density of unmanned vehicles and communication interference caused by many communicating vehicles arise: these tradeoffs may require to reduce the communication load to avoid

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Section: Distributed Adaptationmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

Baldi

Sun

Zhou

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…5. Evidently, the proposed method can improve the transmission accuracy of the WAVE system while compensating the filtering delay and system propagation delay, allowing the data obtained from the WAVE system to reflect the real situation of the target to the largest extent [22].…”

Section: Fig4 Improved Nlms Adaptive Filter Processing Results and Fi...mentioning

confidence: 99%

WAVE communication based on improved NLMS adaptive filtering delay compensation method*

Wu¹,

Lee²

2022

J. Phys.: Conf. Ser.

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In order to improve the signal quality of the Wireless Access Vehicle Environment (WAVE), in this paper, the dynamic step factor is introduced into the Normalized Least Mean Square (NLMS) algorithm based on multi-step step descent, so that the algorithm has a wide dynamic adaptive range. And an improved NLMS adaptive filtering method is proposed to reduce the noise of the communication signal of the WAVE system. At the same time, by introducing the unit delay operator z −Δ of the adaptive FIR filter and the transmission delay t p of the WAVE system, a delay compensation function is constructed to solve the delay problem in the signal transmission and processing process of the WAVE system and meet the real-time requirement of the system. In this paper, the above method is simulated and tested under typical working conditions of the Cooperative Adaptive Cruise Control (CACC) system. Experimental results show that the NLMS adaptive filtering method can effectively reduce the error between the acquired signal and the expected signal and make the signal waveform smoother by introducing the dynamic step factor. By introducing the filtering delay compensation function into the signal filtering results, it can effectively reduce the adverse effects of the delay problem caused by signal transmission and signal processing on the desired target signal.

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“…Separately, the research community has been actively studying the effects of Connected Vehicles on traffic flow characteristics. For example, studies have examined the effect of CVs on traffic flow in diverse scenarios including signalized intersections [32], bottlenecks [8], highway congestion [33], and urban road networks [34]. Similarly, other research works have studied the impacts on traffic flow arising from distinct implementation schemes including experimental platoons [35], cooperative highway traffic [36], and in mixed traffic flows [37,38], among others.…”

Section: Influencing Traffic Flow With Connected Vehiclesmentioning

confidence: 99%

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

Jerath,

Gayah,

Brennan

2024

PLoS ONE

Self Cite

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We present a novel perspective on how connected vehicles can reduce total vehicular delay arising due to the capacity drop phenomenon observed at fixed freeway bottlenecks. We analytically determine spatial regions upstream of the bottleneck, called zones of influence, where a pair of connected vehicles can use an event-triggered control policy to positively influence a measurable traffic macrostate, e.g., the total vehicular delay at bottlenecks. These analytical expressions are also able to determine the boundaries (called null and event horizons) of these spatial extents, outside of which a connected vehicle cannot positively influence the traffic macrostate. These concepts can help ensure that information is disseminated to connected vehicles in only those spatial regions where it can be used to positively impact traffic macrostates. Some scenarios examined in this study indicate that communication between connected vehicles may be required over a span of several kilometers to positively impact traffic flow and mitigate delays arising due to the capacity drop phenomenon.

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Congestion-Aware Cooperative Adaptive Cruise Control for Mitigation of Self-Organized Traffic Jams

Cited by 16 publications

References 37 publications

Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

WAVE communication based on improved NLMS adaptive filtering delay compensation method*

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles

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