BISOS: Backwards Incremental System Optimum Search algorithm for fast socially optimal traffic assignment

Ivanchev, Jordan; Zehe, Daniel; Viswanathan, V.; Nair, Suraj; Knoll, Alois

doi:10.1109/itsc.2016.7795902

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…This does not necessarily lead to a (justifiable) personal disadvantage, since a city-wide coordination of traffic towards a desired optimisation goal can be advantageous for all vehicles. For example, a route that seems like a detour could still have a shorter travel time through the use of intelligent traffic lights and if all vehicles coordinate in order to reduce congestion along the route [5]. Advantages do not need to be directly related to travel times, but can also include CO 2 emissions, noise, and urban heat generation [6], which in turn can increase the quality of life in the city.…”

Section: Discussionmentioning

confidence: 99%

Smart City-to-Vehicle — Measuring, Prediction, Influencing

Eckhoff¹,

Zehe²,

Ivanchev³

et al. 2017

ATZ Elektron Worldw

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Section: Discussionmentioning

confidence: 99%

Smart City-to-Vehicle — Measuring, Prediction, Influencing

Eckhoff¹,

Zehe²,

Ivanchev³

et al. 2017

ATZ Elektron Worldw

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“…It is aware of all autonomous vehicles in the road network and the current and predictable traffic situation on every road [2], [3]. To optimize travel time and traffic flow, it balances the traffic load on each road by determining the ideal path for each vehicle that travels from a defined origin to a defined destination [2], [5]. For the case of IVHS, the network only consists of the highway.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

Ivanchev¹,

Deboeser²,

Braud³

et al. 2021

IEEE Access

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This paper introduces the a framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres which implements four design principles: Standardisation, Encapsulation, Abstraction, and Decoupling (SEAD). Although a large body of research has been formulating platooning manoeuvres, it is still challenging to design, describe, read, and understand them. This difficulty largely arises from missing formalisation. To fill this gap, we analysed existing ways of describing manoeuvres, derived the causes of difficulty, and designed a framework that simplifies the manoeuvre design process. Alongside, a Manoeuvre Design Language was developed to structurally describe manoeuvres in a machine-readable format. Unlike state-of-the-art manoeuvre descriptions that require one state machine for every participating vehicle, the SEAD framework allows describing any manoeuvre from the single perspective of the platoon leader. We hope that the SEAD framework will pave the way for further research in the area of new manoeuvre design and optimisation by largely simplifying and unifying platooning manoeuvre representation.

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“…It is aware of all autonomous vehicles in the road network and the current and predictable traffic situation on every road [1,2]. To optimize travel time and traffic flow, it balances the traffic load on each road by determining the ideal path for each vehicle that travels from a defined origin to a defined destination [1,4]. For the case of IVHS, the network only consists of the highway.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

Deboeser¹,

Ivanchev²,

Braud³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres. Although a large body of research has been formulating platooning manoeuvres, it is still challenging to design, describe, read, and understand them. This difficulty largely arises from missing formalisation. To fill this gap, we analysed existing ways of describing manoeuvres, derived the causes of difficulty, and designed a framework that simplifies the manoeuvre design process. Alongside, a Manoeuvre Design Language was developed to structurally describe manoeuvres in a machine-readable format. Unlike state-of-the-art manoeuvre descriptions that require one state machine for every participating vehicle, the SEAD framework allows describing any manoeuvre from the single perspective of the platoon leader. We hope that the SEAD framework will pave the way for further research in the area of new manoeuvre design and optimisation by largely simplifying and unifying platooning manoeuvre representation.

show abstract

BISOS: Backwards Incremental System Optimum Search algorithm for fast socially optimal traffic assignment

Cited by 8 publications

References 24 publications

Smart City-to-Vehicle — Measuring, Prediction, Influencing

Smart City-to-Vehicle — Measuring, Prediction, Influencing

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

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