On the design of a route parsing engine for connected vehicles with applications to congestion management systems

Sinnott, Sam; Ordóñez-Hurtado, Rodrigo H.; Russo, Giovanni; Shorten, Robert

doi:10.1109/itsc.2016.7795769

Cited by 11 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We illustrate our results via a simple example inspired by [19], where a parsing engine for connected vehicles was developed to orchestrate the routes of a network of cars (routes were determined by a shared cloud service from the available information). In this context, we now consider the scenario where an agent (i.e.…”

Section: Numerical Examplementioning

confidence: 99%

On the crowdsourcing of behaviors for autonomous agents

Russo

2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper is concerned with the problem of designing, from data, agents that are able to craft their behavior from a set of contributors in order to fulfill some agent-specific task. This is not necessarily known to the contributors. After formalizing this crowdsourcing process as a control problem, we present a result to synthesize behaviors from the information made available by the contributors. The result is turned into an algorithm and the theoretical findings are complemented via an example.

show abstract

Section: Numerical Examplementioning

confidence: 99%

On the crowdsourcing of behaviors for autonomous agents

Russo

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, we recognise that the Markov chain approach would be more efficient in storing details of historical routes of single vehicles. For more details regarding the use of Markov chains for traffic modelling in vehicular applications, see references [12,[18][19][20][21]. Robert Shorten Professor Shorten is currently Professor of Control Engineering and Decision Science at University College Dublin, and holds a position at IBM Research.…”

Section: Appendix Markov Chain Models and Driver Intentionmentioning

confidence: 99%

“…In particular, the work in the present paper builds on [6][7][8]12]. In [6], we had proposed using feedback control theory to regulate pollution level in a geo-fenced urban area.…”

Section: Introductionmentioning

confidence: 99%

“…A severe limitation of this work was that the routes travelled by each vehicle were assumed to be known a priori. This latter issue is addressed in a different context in [12] in which Markov-decision-based route-prediction engines are proposed and validated 1 . Our present work uses elements of all four papers to create a framework in which a meaningful optimisation can be formulated with a view to protect pedestrians.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pedestrian-Aware Engine Management Strategies for Plug-In Hybrid Electric Vehicles

Liu

Naoum-Sawaya

et al. 2018

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

Abstract-Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. While hybrid vehicles are usually designed with the objective of minimising fuel consumption, in this paper we propose engine management strategies that also take into account environmental effects of the vehicles to pedestrians outside of the vehicles. Specifically, we present optimisation based engine energy management strategies for PHEVs, that attempt to minimise the environmental impact of pedestrians along the route of the vehicle, while taking account of route dependent uncertainties. We implement the proposed approach in a real PHEV, and evaluate the performance in a hardware-in-the-loop platform. A variety of simulation results are given to illustrate the efficacy of our proposed approach.

show abstract

“…As personalised driving experience expectations are growing ( [7]), so is the need to automate the prediction of the driver's behaviour. Understanding the driver's intentions is a prerequisite to enabling personalised assistance functions, such as personalised risk assessment and mitigation, speed advice ( [4,10,11]), rerouting ( [14]) infrastructure systems ( [15]), engine management systems ( [5]), etc. Amongst the key driver intentions to predict, destination and route have been the subject of several research efforts, see for example ([13, 16, 2, 3, 9]) and the references therein.…”

Section: Introductionmentioning

confidence: 99%

A hidden Markov model for route and destination prediction

Lassoued

Monteil

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

We present a simple model and algorithm for predicting driver destinations and routes, based on the input of the latest road links visited as part of an ongoing trip. The algorithm may be used to predict any clusters previously observed in a driver's trip history. It assumes that the driver's historical trips are grouped into clusters sharing similar patterns. Given a new trip, the algorithm attempts to predict the cluster in which the trip belongs. The proposed algorithm has low temporal complexity. In addition, it does not require the transition and emission matrices of the Markov chain to be computed. Rather it relies on the frequencies of co-occurrences of road links and trip clusters. We validate the proposed algorithm against an experimental dataset. We discuss the success and convergence of the algorithm and show that our algorithm has a high prediction success rate.

show abstract

On the design of a route parsing engine for connected vehicles with applications to congestion management systems

Cited by 11 publications

References 9 publications

On the crowdsourcing of behaviors for autonomous agents

On the crowdsourcing of behaviors for autonomous agents

Pedestrian-Aware Engine Management Strategies for Plug-In Hybrid Electric Vehicles

A hidden Markov model for route and destination prediction

Contact Info

Product

Resources

About