Comparing the Observable Response Times of ACC and CACC Systems

Brunner, Johannes S.; Makridis, Michail; Kouvelas, Anastasios

doi:10.1109/tits.2022.3165648

Cited by 27 publications

(7 citation statements)

References 24 publications

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“…• Enhanced connectivity with V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) communication, to ensure safe and quick response to perturbation events much further downstream in a timely manner, and thus, generating smoother responses, see e.g. [33].…”

Section: Discussionmentioning

confidence: 99%

Energy-Based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons

Apostolakis

Makridis²,

Kouvelas³

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Section: Discussionmentioning

confidence: 99%

Energy-Based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons

Apostolakis

Makridis²,

Kouvelas³

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…The reaction time T and time gap t g are derived from the empirical characterizations carried out in Brunner et al ( 44 ) where the same method was enforced to estimate T for both ACC and CACC controllers using experimental evidence. It is worth noticing that the CACCs have a reaction time that is half of the corresponding ACC realization.…”

Section: Methodsmentioning

confidence: 99%

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

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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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“…The last important factor for spacing policy is the reaction time of drivers in case of more or less dangerous maneuvers [7,34,35]. They, in fact, vary depending on whether it is to react to an obstacle on the road, a vehicle that crosses a junction to a car that brakes in front of us.…”

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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Comparing the Observable Response Times of ACC and CACC Systems

Cited by 27 publications

References 24 publications

Energy-Based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons

Energy-Based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons

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

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

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