# Language of Driving for Autonomous Vehicles
## 1 Introduction  
Autonomous vehicles (AVs) are one of the dominant topics in engineering research, and a large number of private and academic organizations are investing resources to develop effective autonomous agents that will be populating our streets in the coming years. One major dilemma faced by autonomous cars is understanding the intentions of other road users and how to communicate with them [1]. Though localization, mapping, route planning, and control of AVs are widely studied, and the literature already offers working solutions in static environments, road users are non-static, complex, interactive agents having their own goals, utilities, and decision-making systems [2]; their study is an emerging research topic. Human-AV interaction (HAVI) with pedestrians must take these interactive agents into account in order to predict their actions and plan accordingly. The mode and intent of communication from the AVs to other road users are two fundamental requirements for the future of transportation systems. Clear messages and information are necessary to avoid misunderstandings between vehicles and pedestrians leading to unexpected behaviors, unnecessary vehicle yielding, or dangerous situations. The current driving domain is dominated by human drivers and well-established communication channels based on human-to-human (eye contact, head positions, gestures) and machine-to-human (speed, direction, sound) cues [3]. This is supported by well-established and defined methods to communicate the vehicle status and intentions. This situation will slowly shift to a mixed agent domain, where autonomous vehicles will coexist together with human drivers. The fast development of these technologies foresees a future scenario where autonomous vehicles will dominate road traffic, and the presence of human-controlled agents will be minimal. Nevertheless, the presence of human agents or other road users in the traffic flow will probably endure. In this context, AVs should be able to communicate their status in a clear and understandable way, by making use of specific Human-Machine Interface (HMI) systems and interpreting human decisions by detecting pedestrians' movement cues and behavioral patterns [4]. HMI solutions taking into consideration the new Language of Driving (LoD) between machines and human agents need to be assessed and validated through user-based research studies. Furthermore, different road users' needs and abilities should be taken into consideration. Pedestrians and AV shuttle passengers might vary in different contexts or moments of the day. Scenarios like big hospital compounds, schools, or simply residential districts need to be considered with children, elderly people, and other fragile user groups being involved in testing and assessing HMI solutions for AV buses. This work aims at tackling this topic by presenting results from a real-world experimental study carried out at Tallinn University of Technology campus and involving 176 pedestrians interacting with the ISEAUTO autonomous driving shuttle. For further details about the ISEAUTO autonomous shuttle and connected research, please refer to [5; 6; 7]. We present here the results from the survey collected during the experimental sessions. The survey and interview results are evaluated via focus group interviews with key public sector representatives from the Ministry of Economic Affairs and Communications, the Estonian Transport Administration, and the City of Tallinn. Based on the findings, we propose an architecture for a Mixed Reality (MR) simulation application aimed at designing, testing, and assessing external and internal HMIs for AV shuttle interactions with pedestrians and passengers. This approach can deliver fast prototyping of different design solutions, repeatability, and an inclusive and safe testbed for interface validation with human agents.