Multianticipation for string stable Adaptive Cruise Control and increased motorway capacity without vehicle-to-vehicle communication

Donà, Riccardo; Mattas, Konstadinos; He, Yinglong; Albano, Giovanni; Ciuffo, Biagio

doi:10.1016/j.trc.2022.103687

Cited by 19 publications

(13 citation statements)

References 62 publications

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“…In some cases, in fact, it is possible to define the spacing error, that is, when the actual space moves away from the security space, creating instability in the flow of traffic. In fact, if in a platoon, the error increases, the traffic string becomes unstable, while if the error decreases it means that the string is kept within the considered time interval [29][30][31]. Drivers are not always able to perceive the right safe distance to keep to avoid an accident.…”

Section: Impact Of Acc On Traffic Flowmentioning

confidence: 99%

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

Acerra¹,

Brasile²,

Simone³

et al. 2023

Preprint

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Adaptive Cruise Control (ACC) is useful in the most dangerous maneuvers such as braking and acceleration. This study assesses how ACC modifies traffic flows by analysing the differences be-tween manual and autonomous driving in connected and autonomous vehicles (CAVs). Using a platoon of 80 vehicles, tested in pairs on the road, it was possible to define the speed trends during braking and acceleration and the reaction times to the driving maneuvers (PRT, TH, TCC) with a kinematic data detector. The interactions between the CAV and the driver, have been studied in-novatively, i.e through gaze analysis. Situations of potential danger, characterized by the braking of the vehicle that precedes the car with the driver equipped with the eye tracker tool, have been recreated, considering the influence of the driver’s ACC experience. Results statistically confirmed that with the ACC switched on the reaction times are greater than manual driving (2.4/3.8 sec); this can lead to a reduction in road safety, further motivated by the rapid decrease in speed. The interpolation between automated and human data, finally, has allowed the detection of some criticalities of the system that are fundamental in order to reach the second level of automation.

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Section: Impact Of Acc On Traffic Flowmentioning

confidence: 99%

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

Acerra¹,

Brasile²,

Simone³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, multianticipation has been shown to increase the string stability of a platoon even under long reaction times ( 33 , 34 ). Eventually, an ACC-oriented micro-simulation model for multianticipation was formulated and analyzed in Donà et al ( 20 ) which we take here as a reference model for the M-ACC realization. Evidence suggesting that radar can perceive objects that are not in direct line of sight, thus enabling multianticipation in car-following applications, has been presented in Scheiner et al ( 35) and Thai et al ( 36 ) for a similar case of study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The realization of the enhanced models stems from the linear car-following model which is extended to accommodate the second leader anticipation in the M-ACC, analogously to Donaè t al. (20), and communication retrieved information in the CACCs. We selected available experimental evidence on ACC calibration (5,22,23) to derive the gains for the models for the sake of realism.…”

mentioning

confidence: 99%

“…That was the status quo until vehicles capable of reacting to more than one leader without the use of connectivity approached the market ( 19 ). Following a car manufacturer declaring that its ACC would be capable of tracking additional leaders ( 19 ), in Donà et al ( 20 ) we presented a simulation model which allows quantification of the effectiveness of radar-based multianticipation (M-ACC) via the augmentation of the extensively adopted linear car-following model. The findings motivated the authors to go deeper into the performance evaluation and to study how such a system would stand up against CACC.…”

mentioning

confidence: 99%

“…Their effectiveness is compared in heterogeneous scenarios where a baseline ACC car-following model is progressively substituted by either the M-ACC or one of the considered CACC topologies. The realization of the enhanced models stems from the linear car-following model which is extended to accommodate the second leader anticipation in the M-ACC, analogously to Donà et al ( 20 ), and communication retrieved information in the CACCs. We selected available experimental evidence on ACC calibration ( 5 , 22 , 23 ) to derive the gains for the models for the sake of realism.…”

mentioning

confidence: 99%

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Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Donà

Mattas

Albano³

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

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The paper explores via simulation analysis how multianticipative adaptive cruise control (M-ACC) can address traffic oscillations with respect to cooperative adaptive cruise control (CACC) approaches. The work is grounded on our previous findings where we suggested a functioning logic and a performance characterization of M-ACC. That activity followed a manufacturer’s claim that vehicles capable of reacting to more than one leader ahead are already populating public roads. In this light, M-ACC represents an intermediate solution between the traditional ACC, which typically is capable of reacting to only one vehicle ahead, and connectivity-enhanced approaches, which in principle could track multiple leaders. Given that M-ACC is effective from small penetration rates—as it does not require the presence of other M-ACC-equipped vehicles to reach its full potential—performance comparison with CACC is not straightforward. In particular, for a fair comparison it is necessary to quantify the performance gap as a function of the penetration rate, which is exactly the objective of this paper. Three CACC topologies are investigated and both stochastic simulations and Pareto analyses are performed. The results demonstrate that, for small market penetration figures, M-ACC can still guarantee better performances than any CACC heterogenous platoons for all the metrics considered given that it does not require infrastructure or other adjacent vehicles featuring the same technology. Considering that the market adoption of connectivity-based solutions is facing challenging practical issues, M-ACC could represent a suitable as well as realistic option to harvest the benefits of automation to traffic flow in the near future.

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Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free

Liu

Sun

et al. 2022

Neural Comput & Applic

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Multianticipation for string stable Adaptive Cruise Control and increased motorway capacity without vehicle-to-vehicle communication

Cited by 19 publications

References 62 publications

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

Traffic Flow with the Adaptive Cruise Control: The Comparison between Autonomous and Manual Driving

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free

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