Enabling automated driving by ICT infrastructure : a reference architecture

Buchholz, Michael; Strohbeck, Jan; Adaktylos, Anna-Maria; Vogl, Friedrich; Allmer, Gottfried; Röggla, Thomas; Lassoued, Yassine; Wimmer, Markus; Hätty, Birger; Massot, Guillemette; Ponchel, Christophe; Bretin, Maxime; Sourlas, Vasilis; Amditis, Angelos

doi:10.18725/oparu-26023

Cited by 1 publication

(2 citation statements)

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“…Additionally, the accumulation is done using weighted rewards, and all the collected statistics are reset once the confidence bound reaches a certain minimum value. As a result, MUBEV-SM can better cope with environments whose reward distribution dynamically changes or includes long periods of stationarity 3 . However, the MWRM mechanism can be disabled if required (MW RM = False in Algorithm 1).…”

Section: Optimal Policy Search Algorithmmentioning

confidence: 99%

“…In alignment with this, the smart parking solution proposed in this paper aims to use moving vehicles as collectors and processors of raw environment data so that PSA can be shared as floating car data. For instance, if the ICT architecture proposed in [3] is integrated with the Spatial Potisioning Token (SPToken) framework [4,5], then SPToken observers could include connected automated vehicles (CAVs), road-side units (RSUs), or MEC servers, and the floating car data can be transmitted via (perhaps extended) Cooperative Awareness Messages and Collective Perception Messages [6] (or a new message type to be developed). This paper is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Positioning Token (SPToken) for Smart Mobility

Overko

Ordóñez-Hurtado

Zhuk

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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In this paper, we describe an approach to guide drivers searching for a parking space (PS). The proposed system suggests a sequence of routes that drivers should traverse in order to maximise the expected likelihood of finding a PS and minimise the travel distance. This system is built on our recent architecture SPToken, which combines both Distributed Ledger Technology (DLT) and Reinforcement Learning (RL) to realise a system for the estimation of an unknown distribution without disturbing the environment. For this, we use a number of virtual tokens that are passed from vehicle to vehicle to enable a massively parallelised RL system that estimates the best route for a given origin-destination (OD) pair, using crowdsourced information from participant vehicles. Additionally, a moving window with reward memory mechanism is included to better cope with non-stationary environments. Simulation results are given to illustrate the efficacy of our system.

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Section: Optimal Policy Search Algorithmmentioning

confidence: 99%