Braking in Close Following Platoons: The Law of the Weakest

Murthy, Dharshan Krishna; Masrur, Alejandro

doi:10.1109/dsd.2016.78

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…However, it should also be noted that a human driver would brake differently given full, partial, or no information available about the hazard and the strategies of the other drivers. Murthy and Masrur propose the law of the weakest, i.e., the whole platoon should tune its maximum deceleration to the one with the weakest braking capacity [54]. As the authors use predecessor following communication topology, the law of the weakest strategy can be placed in the DPI or the DOI state in Figure 3.…”

Section: State-of-the-art Braking Strategiesmentioning

confidence: 99%

Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning

Hasan

Girs

Uhlemann

2023

IEEE Open J. Intell. Transp. Syst.

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The benefits of platooning, e.g., fuel efficiency, road throughput enhancement, driver offload, etc., have sparked an interest in a more connected, intelligent, and sustainable transportation ecosystem. However, efficient platooning is realized through wireless communications, characterized by transient connectivity, which is caused by occasional packet losses. Being a safety-critical system of systems, a platoon must be fail-operational even during transient connectivity. Moreover, a platoon should be capable of transitioning into a fail-safe state upon encountering a hazard. To this end, we propose a strategy for classifying the transient communication outages incurred by platooning vehicles into states. Furthermore, a state machine using these states to enable safe automated platooning is proposed that also defines the transitions between the states based on the nature and levels of transient connectivity and hazards. To achieve this, a graceful degradation and upgradation method is proposed, such that the platoon can remain fail-operational by adjusting, e.g., the automated controller and/or the inter-vehicle gaps based on the current communication quality. An emergency braking strategy is also proposed to enable a fast transition into a fail-safe state, should the platoon encounter a hazard. Rigorous simulation studies show that the proposed strategies enable fault-tolerant automated platooning also during transient connectivity.

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Section: State-of-the-art Braking Strategiesmentioning

confidence: 99%

Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning

Hasan

Girs

Uhlemann

2023

IEEE Open J. Intell. Transp. Syst.

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“…Thus, in this study the minimum inter-vehicle distance was chosen to be 0.25 times the car length. In order to address the risk of collision at such small inter-vehicle distances, Dharshan et al [39] present an experimentally validated approach to optimize the braking system of autonomous vehicle platoon considering the vehicle loading condition for executing a collision free braking maneuver. Moreover, the close/narrow inter-vehicle distance, used in vehicle platoon studies [28], [38] and in this study, have been substantiated by the recent advancements in autonomous vehicle ecological cooperative control [40], [41] coupled with IoT [42], [43] to ensure passenger safety.…”

Section: A Aerodynamic Study Of Vehicle Platoonmentioning

confidence: 99%

Prediction of Drag Force on Vehicles in a Platoon Configuration Using Machine Learning

et al. 2020

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Machine learning is used for extraction of valuable information from data thus helping in exploration of hidden patterns, leading to learning models that can be used for prediction. In the domain of autonomous vehicles machine learning techniques have been applied in several areas, vehicle platooning being one of them. Vehicle platooning is a vital feature of automated highways which provides the key benefits of fuel economy, road safety and environmental protection coupled with safe road transportation. However, high computational cost associated with the numerical simulation of vehicle aerodynamics makes the Computational Fluid Dynamics (CFD) study of vehicle platoon prohibitively expensive and complex. Machine learning, with its high predictive power, has emerged as a promising compliment to CFD studies of external aerodynamics. This paper presents estimation error based performance comparison of five different supervised learning algorithms: Support Vector Regression, Polynomial Regression, Linear Regression and two different models of Neural Networks for prediction of aerodynamic drag coefficient corresponding to each vehicle in a two, three and four vehicle platoon configurations based on the drag coefficients provided by experimental study at different inter-vehicle distances. Predicted drag coefficients are then juxtaposed with CFD data from numerical simulations to evaluate closeness to experimental drag coefficients. Results reveal that polynomial regression model best fits the aerodynamics with 0.0223 estimation error. To the best of our knowledge no machine learning based methods have been applied before for modeling aerodynamic drag on vehicle platoon.

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“…Murthy et al [10] propose a simulated environment designed to have cars traveling in a highway, self-organize themselves in platoons with the final goal to reduce fuel consumption, by drafting off one another. This approach goes in the same direction of the previous one, hence it is aimed to achieve re-usability and predictability in a specific application scenario.…”

Section: Coordination Of Autonomous Vehiclesmentioning

confidence: 99%

Exploiting Traffic Lights to Manage Auction-Based Crossings

Muzzini

Capodieci

Montangero

2020

Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good

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Auction-based crossing management approaches are used to design coordination policies for autonomous vehicles and improve smart intersections by providing differentiated latencies. In this paper, we propose and exploit an auction based mechanism for managing the urban traffic light infrastructure in which participant vehicles are either equipped or non-equipped. The difference between these two categories of vehicles is that only the equipped ones can actively participate to auctions through in-vehicle IoTdevices, i.e. they are able to communicate with the surrounding urban infrastructure. In this way, we aim to study the transitional period that will occur before the complete adoption of autonomous or strongly connected vehicles. Through extensive experiments and simulations, by comparing our mechanism to the traditional traffic light fixed-time-control approach, we studied the benefits and limitations, in term of waiting and trip times, when varying the subset of equipped vehicles and the available budget that can be used to participate to auctions.

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Braking in Close Following Platoons: The Law of the Weakest

Cited by 17 publications

References 11 publications

Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning

Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning

Prediction of Drag Force on Vehicles in a Platoon Configuration Using Machine Learning

Exploiting Traffic Lights to Manage Auction-Based Crossings

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