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.

Microscopic agent-based traffic simulation is an important tool for the efficient and safe resolution of various traffic challenges accompanying the introduction of autonomous vehicles on the roads. Both the variety of questions that can be asked and the quality of answers provided by simulations, however, depend on the underlying models. In mixed traffic, the two most critical models are the models describing the driving behaviour of humans and AVs, respectively. This paper presents AVDM (Autonomous Vehicle Driving Model), a hierarchical AV behaviour model that allows the holistic evaluation of autonomous and mixed traffic by unifying a wide spectrum of AV functionality, including long-term planning, path planning, complex platooning manoeuvres, and low-level longitudinal and lateral control. The model consists of hierarchically layered modules bidirectionally connected by messages and commands. On top, a high-level planning module makes decisions whether to join/form platoons and how to follow the vehicle's route. A platooning manoeuvres layer guides involved AVs through the manoeuvres chosen to be executed, assisted by the trajectory planning layer, which, after finding viable paths through complex traffic conditions, sends simple commands to the low-level control layer to execute those paths. The model has been implemented in the BEHAVE mixed traffic simulation tool and achieved a 92% success rate for platoon joining manoeuvres in mixed traffic conditions. As a proof of concept, we conducted a mixed traffic simulation study showing that enabling platooning on a highway scenario shifts the velocity-density curve upwards despite the additional lane changing and manoeuvring it induces.

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.