Technical framework and data sharing for mixed fleet situational awareness

Abstract

This study introduces an innovative technical framework for mixed fleet situational awareness (SA) aimed at optimizing material flow in factories. The framework integrates a range of digital solutions, including positioning technologies, a universal fleet control system, diverse user interfaces, advanced machine perception, and tools for generating predictive insights - such as potential traffic congestion and hazardous zones. The findings support digital transformation of factory logistics and offers novel insights into SA in mixed fleets, extending beyond prior research focused predominantly on autonomous or multi-robot systems. It also proposes data-sharing models tailored to different user needs: an operator view for factory-wide SA to support e.g. traffic planning, and a first-person view for factory workers, offering real-time information on close-by machine status, warnings, and intentions. Innovating new solutions for mixed fleet situational awareness will be a key driver of digital transformation in factory logistics.

Headnote

Abstract: This study introduces an innovative technical framework for mixed fleet situational awareness (SA) aimed at optimizing material flow in factories. The framework integrates a range of digital solutions, including positioning technologies, a universal fleet control system, diverse user interfaces, advanced machine perception, and tools for generating predictive insights - such as potential traffic congestion and hazardous zones. The findings support digital transformation of factory logistics and offers novel insights into SA in mixed fleets, extending beyond prior research focused predominantly on autonomous or multi-robot systems. It also proposes data-sharing models tailored to different user needs: an operator view for factory-wide SA to support e.g. traffic planning, and a first-person view for factory workers, offering real-time information on close-by machine status, warnings, and intentions. Innovating new solutions for mixed fleet situational awareness will be a key driver of digital transformation in factory logistics.

Keywords: Mixed fleet; situational awareness; data; fleet control; autonomous systems; AGV; AMR.

1 Introduction

This study focuses on industrial mixed fleets, comprising mobile machines that operate indoors within factories and warehouses. These fleets are primarily used for material handling tasks and include Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), forklift trucks, and overhead cranes. However, a significant challenge is that factories often lack Situational Awareness (SA) regarding their mixed fleets e.g., where the machines are located, what tasks they are performing, and what their next actions will be. As a result, the material flow within the factory cannot be optimized in a holistic manner, leading to decreased efficiency, reduced profitability, and safety risks. While various digital technologies are utilized in internal logistics, solutions that provide SA at the mixed fleet level remain limited. To address this, there is a clear need for innovative solutions, a deeper understanding of the required technical solutions, and the role of data in enabling effective mixed fleet SA.

Digital solutions for autonomous fleets are plentiful enabling functions like positioning, autonomous navigation, fleet control, and collision avoidance. These solutions rely heavily on data. However, for manually operated machines such as forklift trucks and overhead cranes, fleet-level control and optimization have been less prevalent. To achieve holistic mixed fleet-level SA, a new technical concept is required. A single solution cannot address the central challenges; instead, a combination of multiple technical enablers must be integrated into an integrated solution (Davies, 1997; Hakanen, 2014). In the digital innovations domain, this represents a combinatorial innovation, which means that a new digital solution is created by combining existing modules with embedded digital capabilities (Ciriello et al., 2018).

We adopt a Design Science Research (DSR) approach (Hevner et al., 2004; Herbert, 1988; March & Smith, 1995), which involves the development of a technical framework and data-sharing models tailored for mixed fleet SA. Based on qualitative data collected through interviews (n = 21) and a workshop, the study identifies key technical enablers for achieving SA in mixed fleets, outlines the types of data and information shared machine-to-machine (M2M) and machine-to-human (M2H), and defines two levels of mixed fleet SA relevant to different user groups.

2 Theory

2.1. Mixed fleet situational awareness

This study focuses on industrial mixed fleets, comprising mobile machines that operate indoors within factories and warehouses. These fleets are primarily used for material handling tasks and include Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), forklift trucks, and overhead cranes. In essence, some of these machines navigate autonomously, while others require manual control. Moreover, in our case, factory and warehouse workers - humans - share the same operational space with these mixed fleet machines.

We propose that achieving Situational Awareness (SA) at the mixed fleet level is a fundamental prerequisite for optimizing operations and ensuring safety in factory environments (Hakanen et al., 2024). Situational Awareness is commonly defined through three key phases: (1) the perception of environmental elements within a given time and space, (2) the comprehension of their meaning, and (3) the projection of their status in the near future (Endsley, 1995). Originally developed in the 1990s within the aviation domain - where pilots rely on SA to operate aircraft safely (Jensen, 1997) - the concept has since been extended to autonomous multi-robot systems (Asmar et al., 2020; Bavle et al., 2023; Dahn et al., 2018; Gómez, 2018). Recently, we have applied the SA framework to industrial mixed fleets, identifying key activities - such as mixed fleet localization - necessary for establishing fleet-level situational awareness (Hakanen et al., 2024).

AGVs and AMRs require SA to navigate autonomously and avoid collisions with other machines, physical structures, and humans (Asmar, 2020; Bavle et al., 2023; Gómez, 2018; Liu et al., 2021). Their autonomous navigation typically depends on Simultaneous Localization and Mapping (SLAM), a technique that supports perception, mapping, positioning, and collision avoidance (Bloss, 2008; Fragapane et al., 2021; Harapanahalli, 2019). By employing SLAM, AGVs and AMRs continuously measure distances to surrounding objects, determine their position relative to the map and environment, and thereby generate the SA necessary for safe and efficient navigation. For instance, when Light Detection and Ranging (LiDAR) sensors detect a human approaching, the machine responds by slowing down or stopping, depending on the proximity. In alignment with the SA framework, these autonomous machines use sensors to perceive their environment, "comprehend" the significance of detected elements, and project their future actions accordingly.

2.2. Fleet control systems for situational awareness

In addition to the machine-level SA achieved through various sensors and cameras, it is equally important to establish situational awareness at the fleet level. For autonomous fleets, this is typically facilitated through fleet control systems, which serve as user interfaces that allow human operators to monitor and manage overall operations. These systems generally include a mission or task planner, a mapping module, interfaces for integrating sensor data, and connections to a group of autonomous machines, along with a user interface for the operator (Quadrini, 2020). Through the user interface, the operator gains a comprehensive overview of the entire autonomous fleet - tracking machine locations, monitoring ongoing tasks, identifying potential problems, and more.

AGVs and AMRs are typically managed through fleet control systems provided by their respective Original Equipment Manufacturers (OEMs). A key challenge in this context is the fragmentation of these systems and the siloed nature of information related to machine status, locations, and task execution. Compounding the issue, manually operated machines are not integrated into any centralized system - their locations, routes, and relevant data for traffic optimization remain invisible. Consequently, achieving a unified overview of the mixed fleet becomes impossible, making centralized control unfeasible and limiting the potential for fleet-wide optimization and systemic safety management (Hakanen et al., 2024).

The publication of the VDA 5050 interface standard by the German Association of the Automotive Industry (Verband der Automobilindustrie, VDA) in 2020 marked a significant milestone toward enabling centralized control of heterogeneous fleets of autonomous machines (VDA, 2020). Since its introduction, the adoption of VDA 5050 in industrial internal logistics has steadily increased (Hakanen et al., 2022; Franke et al., 2023). VDA 5050 defines an open, standardized communication interface for AMRs, AGVs, and fleet management systems. It specifies the required data types, messages, and status monitoring (VDA, 2020).

Fleet control systems that comply with VDA 5050 significantly streamline the operator's daily responsibilities by enabling centralized monitoring and control of the entire fleet through a unified system and user interface (Hakanen et ak, 2022). These systems also enhance operational efficiency and safety by supporting optimized task allocation and traffic management at the fleet level (Hakanen et al., 2022). Importantly, such standardized systems offer substantial benefits in mixed fleet environments, where both autonomous and manually operated machines - such as forklift trucks and overhead cranes - are in use.

2.3. Role of human-centred design

Since the operator gains an overview of the mixed fleet through a unified user interface, adopting a human-centred design approach is especially important. In user interface design, the Human-Centered Design (HCD) process is highly applicable. According to the ISO 9241-210 standard (ISO, 2019), HCD focuses on users, their needs, and requirements, with the goal of enhancing effectiveness, efficiency, and improving human well-being, satisfaction, accessibility, and sustainability. A key principle of HCD is the iterative involvement of end users throughout the design process, which includes phases such as planning, understanding the context of use, specifying user requirements, developing design solutions, and evaluating those solutions (ISO, 2019). The design process should ultimately strive for optimal usability.

This is particularly critical in the operator interface, where users must quickly perceive essential information about the entire fleet and identify potential errors or problem situations. Usability guidelines - such as those outlined by Nielsen (1994) - emphasize that interfaces should minimize cognitive load and support users in perceiving the system's status. Therefore, designing an SA interface for mixed fleet operators must follow foundational usability principles, including "visibility of system status," "consistency and standards," "error prevention," "recognition rather than recall," and "aesthetic and minimalist design."

2.4. Research gaps and questions

To summarize the literature review, prior research has applied the concept of situational awareness (SA) to autonomous and multi-robot systems (Asmar et al., 2020; Bavle et al., 2023; Dahn et al., 2018; Gómez, 2018). However, empirical studies specifically addressing situational awareness in mixed fleets and related technologies remain limited. Moreover, there is a growing need for managerial insights to help companies manage material flows more efficiently and safely.

In response, this study seeks to extend the SA concept by exploring its application within mixed fleets and identifying the technical enablers and data-sharing models that support M2M and M2H communication. This research builds on our earlier work, which identified shared perception, centralized control of mixed fleets, and the localization of both machines and humans as key SA activities in such environments (Hakanen et al., 2024). Our aim is to deepen the understanding of SA in mixed fleets by developing a technical framework and data-sharing models to support M2M and M2H interactions. Thus, in addition to contributing to the theoretical body of knowledge, this study seeks to lay the foundation for practical solutions that optimize mixed fleet operations and enhance safety. Accordingly, the research questions guiding this study are:

1. What technical solutions constitute the technical framework for mixed fleet situational awareness (SA)?

a. What are the key technical enablers required for achieving mixed fleet SA

2. What types of data sharing models can be identified for mixed fleet SA?

a. What situational awareness data and information should be shared between machines (M2M) and between machines and humans (M2H)?

b. What types of situational awareness information are relevant to different user groups?

3 Methodology

3.1 Research approach and participants

This study adopts the DSR approach, a pragmatic methodology commonly used in the field of information systems. DSR focuses on developing technology-based solutions to address significant and relevant business challenges (Hevner et al., 2004; Hevner, 2007). It has been widely applied in disciplines such as computer science, software engineering, and information systems for several decades (Herbert, 1988; March & Smith, 1995). Central to the DSR approach are the artefacts that are created - such as constructs, models, and methods - which are used in the development and application of information systems (Hevner et al., 2004). DSR can be viewed as an iterative process that bridges the gap between theory and practice (Holmström et al., 2009). The DSR approach was selected because it aligns with the study's objectives. The primary goal of the study was to develop a technical framework and data-sharing models for shared situational awareness in mixed fleets. These artefacts were developed and examined through close collaboration between the industry and academia. In total seven organizations participated in the study as a part of a large Mixed Fleet co-innovation project (20232026) as listed in Table 1:

This study was conducted between January and December 2024. The goal of obtaining rich insights and diverse perspectives from different companies and mixed fleets guided the selection of participants. The companies involved in the project represent various sectors, including two large crane and lifting equipment manufacturers, a medium-sized forklift truck and AGV manufacturer, two small-sized enterprises specializing in fleet control systems and automation software. All participating companies are involved in material handling business and mixed fleets, working together to address shared challenges in this area. In addition to industry-academia collaboration, research collaboration took place between two research institutes: Technical Research Centre of Finland (VTT) and Tampere University (TUNI).

3.2. Study phases and execution

The study was carried out through the following phases:

1. In-depth interviews with representatives from industrial companies (n=21): gathering managerial insights on future mixed fleet technical solutions and data sharing (VTT)

2. Data analysis of qualitative interview data (VTT)

3. Solution/artifact creation: draft of the technical framework for mixed fleet situational awareness (VTT & TUNI)

4. Workshop among the consortium partners (n=ll): collection of additional data, feedback on and refinement of the technical framework, and co-innovation of technical solutions (VTT)

5. Technical framework and data-sharing models descriptions (VTT)

6. Reflection of the results and reporting (VTT and TUNI)

The study followed an iterative process, beginning with qualitative data collection through in-depth interviews. Participants provided informed consent and were briefed on the data privacy terms prior to the interviews. In total, 21 interviews were conducted (see Table 2), with participants selected from diverse operational and organizational units within their respective companies. The interviews were guided by open-ended, thematic questions focusing on potential future technical solutions for mixed fleets and the role of data. Each interview lasted approximately one to one and a half hours. All interviews were recorded and transcribed, and the data was anonymized to ensure that neither individual companies nor interviewees can be identified in the empirical findings.

The interview data was analysed using content analysis (Mayring, 2000; Kohlbacher, 2006; Neuendorf, 2017). Thus, the data was examined through a category system, which facilitated the extraction of relevant aspects from the interview responses (Mayring, 2000). For this study, the data were categorized into the following groups following the interview topics: traffic organization solutions; sensors, data, and data utilization; software solutions; ideas and development needs for improved situational awareness; and ideas for new safety solutions. In content analysis, the data was condensed to its essence by identifying key statements, which resulted in a significant reduction of the data while preserving the original message and offering insights relevant to the research questions (Kohlbacher, 2006). The interview transcripts were reviewed, and illustrative quotations were selected, categorized in a table, and incorporated into the empirical findings section of the paper to enhance transparency of the findings (Yin, 2003).

The empirical insights gathered from the interview round formed the foundation for creating the design artifact, specifically the first draft of the technical framework for mixed fleet situational awareness. This framework was then used as the basis for a situational awareness workshop, which involved six participating organizations and four VTT researchers who led the group discussions. Workshops were used for additional data collection and complementing and refining the technical framework. In total 11 persons participated the workshop. Seven participants took part in both the interviews and the workshop, while four participated only in the workshop. Table 3 outlines the participants in the workshop:

The participants were divided into two groups in the workshop, each tasked with elaborating on the framework and brainstorming future technical solutions and data sharing means. The workshop was documented by Postit notes written by the participants and by recording and transcribing the discussions. The data analysis conducted on the interviews was cross-checked and complemented with the findings from the workshop. Additional insights were incorporated into the findings section.

4 Empirical findings

4.1. Technical enablers for mixed fleet SA

4.1.1 Sensors and mixed fleet positioning solutions

A central material handling problem identified by the informants is that manual and autonomous traffic cannot be optimized as an entity. As a company participant in the workshop described: "Manual process doesn't know what is where and where it should be going. And when automatic process goes to pick up the pallet, it is no longer there and both processes stop". In other words, positioning solutions for both autonomous and manual machines are needed. The interviewees also recognized that the ability to holistically optimize the mixed fleet brings value to the customer, and that there is already a growing market demand for it: "That's interesting that the customers are already asking for manual machine tracking". In fact, the studied companies have already used Ultra-Wide Band (UWB) based tracking system to track forklift trucks. By combining this with e.g. SLAM, mixed fleet SA in terms of machine locations can be achieved.

In addition to using technical solutions for knowing the positions of all mixed fleet machines, the companies raised an idea of tracking also humans as an enabler for enhanced SA. UWB tags are easy to attach to any vehicle, asset or human and they can be used as complementing safety solution in collision avoidance. The UWB tag of a forklift driver, for instance, vibrates when an autonomous machine or a human is nearby. What is notable, though, is that UWB solutions are not safety classified, so they can merely be regarded as complementing to the safety classified safety devices such as e.g. LiDARs in AGVs. Another safety related use case for UWB tracking was also recognized in terms of ensuring that there are needed number of humans on certain areas: "In factory there are some jobs that you can do only with two persons, or you have to have like two persons in the test area. So we had these UWB tags. And if only one guy was in that area, it started to alarm or something". On the other hand, with overhead cranes, it is critical to always ensure that there are no humans underneath. In the future, human tracking solutions may raise new opportunities ensuring safety in lifting situations. However, the GDPR issues need to be handled when collecting information on humans.

In addition to ensuring smooth traffic and safety, positioning solutions enable machine-to-machine collaboration as one informant describes: "Crane that knows its own position and we have ACT that knows its own position, we could make them go to same place if they have the same coordinate system ". Consequently, transferring position data over fleet control system between e.g. an overhead crane and an AGV enables collaboration and load transfer between the machines.

In the workshop discussions, the company representatives viewed that in the future enhanced perception enables gaining rich, close to real-time, in-depth understanding of the factory and warehouse surroundings and conditions. In addition to pure data, objects can be identified and classified, humans detected, characteristics of objects will be known, e.g. whether it is permanent or temporary when blocking some machine's way. Also from the warehouse more information can be gained like mislocated or broken pallets and products etc. Modern sensors and machine vision solutions can thus create situational awareness, which can be shared both between machines and for humans.

4.1.2 Fleet control systems

In addition to various sensors and positioning solutions, fleet control systems are very central technical enablers in automated internal logistics. A central aim in the project has been to view material handling at the mixed fleet level, meaning also including manual traffic in traffic optimization. It was discussed in the project consortium, that in the future, also forklift drivers would receive route suggestions or at least information on e.g. blocked or jammed areas in the factory in order to select the fastest route: "A future vision could be that if we know that there is an AGV stuck in that corridor, we could get the information to the driver that use whatever route you want to, but please avoid that blocked corridor... Google Maps type of a thing, that some roads are in red, it is not wise to drive that way, it takes this and this much time ". There is a need for getting predictive information at the whole factory and warehouse level, of potential problems and jams, in order to avoid those and reroute the fleet. Localization of any anomalies and disturbances of traffic could be used for dynamic fleet control: "The machines which are driving a field they notice, somebody left a trash can or trash box or whatever here which blocks the whole aisle or somebody left a pallet there so it... it puts a time and location stamp for it and then that information can go to fleet control system ".

"Universal" mixed fleet control system was identified as central technical enabled for mixed fleet situational awareness. The studied companies have been applying VDA 5050 interface standard in /order to integrate various OEM's machines and also UWB tracked vehicles and humans into the fleet control system. As future development direction, it was seen, that mixed fleet control system can divide material handling resources in an increasingly flexible and efficient way between manual and autonomous machines: "When we have somewhere a traffic jam or whatever, that the mixed fleet is capable of recalculate the flow and if we see that there is an emergency that we actually go off something that is more flexible than the AGV". The company representatives innovated that in the future, safety zones could be "drawn" in the fleet control system and the system could dynamically reroute the fleet. In emergency situations, such as fires, fleet control system tells the machines to go to predefined safe locations. The system could also include features related to charging planning and economic driving. In the future, user interface could be able to show everything in 3D.

To summarize Chapters 4.1.1 and 4.1.2, potential technical enablers for mixed fleet SA are:

* Fleet control system for task assignment, route planning, and traffic jam or blockage prediction

* LiDAR scanners for measuring distances for collision avoidance and navigation, object detection (e.g., pallets), and identifying lifting locations (e.g., for cranes),

* Cameras and computer vision for navigation and object and anomaly detection

* UWB beacons and tags for tracking manual machines and human operators and vibrating patches for giving warnings or ensuring humans' right and safe locations

* Reflectors to support navigation

* Indicator lights on machines

* Warning sounds

* Screens

* Mobile applications for forklift operators

* Audio-based instructions for forklift operators.

4.2. Mixed fleet related SÄ data and information

In addition to the high-level technical concepts and individual enablers, the informants also shared insights on situation awareness (SA) data and information, as well as the sharing of such data and information M2M and M2H. Next, the empirical findings regarding identified SA data and information are outlined in Table 4:

* Sustainability data related to loads (e.g., CO2 emissions)

* Safe handling of fragile loads including acceleration sensors

* Monitoring human stress levels

5. Conclusions

5.1. Scientific and managerial contributions

5.1.1 Technical framework for mixed fleet SA

We identified a comprehensive set of SA data and information relevant to mixed fleet material handling, along with associated technical solutions. Through analysis and synthesis of the empirical findings, five key technical enablers emerged as particularly vital for optimizing traffic flow and ensuring safety in mixed fleet environments. Accordingly, this study culminates in the development of a technical framework for mixed fleet situational awareness (Figure 1).

The developed technical framework outlines key enablers for situational awareness (SA) in mixed fleet operations:

1. Comprehensive positioning of all fleet actors using complementary technologies such as SLAM and UWB beacons and tags.

2. Universal fleet control system to manage and optimize multi-vendor machine fleets, enabled by standardized interfaces like VDA 5050.

3. Shared situational awareness M2M and M2H, facilitated through fleet control systems and diverse user interfaces (e.g., screens, mobile apps, indicators).

4. Advanced perception capabilities for navigation and collision avoidance, as well as anomaly, object detection, and classification - leveraging technologies such as LiDAR, cameras, and computer vision.

5. Predictive information delivery regarding potential blockages, traffic congestion, hazardous zones, and crane operating areas.

Thus, the study contributes to the literature concerning digital innovations (Ciriello et al., 2018) by presenting a new integrated solution for mixed fleet SA that combines various perception, positioning, fleet control systems, and user interfaces. The technical framework represents a combinatorial innovation, where a new digital solution is created by integrating existing modules with embedded digital capabilities (Ciriello et al., 2018). Depending on the specific characteristics of the factory and the mixed fleet, different combinations of these solutions may be relevant and appropriate. This study also contributes to previous research on SA in autonomous multi-robot systems (Asmar et al., 2020; Bavle et al., 2023; Dahn et al., 2018; Gómez, 2018) by offering empirical insights into SA within mixed fleets - comprising both autonomous and manually operated vehicles - in material handling operations within factory and warehouse environments, where human workers are also present.

The focus of this study is strongly oriented toward co-innovation and future development. Some of the identified solutions - such as positioning systems and fleet control - are already widely implemented, with mature technologies available on the market. In contrast, areas like shared situational awareness, enhanced perception, and the generation of predictive SA information still hold significant potential for further development, despite the existence of some early-stage solutions currently in use. Technologies such as artificial intelligence, particularly machine learning and computer vision, are expected to play an increasingly important role in enabling viable solutions in these domains.

5.1.2 Data sharing models

While previous literature on SA in autonomous and multi-robot systems (Asmar et al., 2020; Bavle et al., 2023; Dahn et al., 2018; Gómez, 2018) has primarily focused on machine-centric SA for autonomous navigation, this study introduces the novel concept of shared SA - both among machines and between machines and humans. Analysis of the findings revealed that different user groups require varying levels of SA. For example, an individual responsible for internal logistics operations requires a high-level overview encompassing the entire factory or warehouse layout, the mixed fleet of machines, route planning, and real-time traffic conditions. In contrast, a worker stationed in a production cell or warehouse area does not need this comprehensive view but instead benefits from localized information about nearby machines - such as their current tasks and intended next actions. Based on these insights, the study proposes a two-level model for mixed fleet situational awareness: The two proposed levels - 1) Operator View and 2) FirstPerson View - are presented in Table 5, along with examples of shared data and information:

5.2. Limitations and future research avenues

One limitation of the study lies in the number of informants. Although over 20 participants provided valuable insights into the topic, involving more informants from various business fields could reveal additional or alternative technical solutions and datarelated aspects. Future research could explore a wider range of potential technologies and examine which combinations would be most effective, as the solutions identified in this study are somewhat complementary and overlapping in their intended use. Since safety must never be compromised, it should be prioritized above all else. Once safety is ensured, various complementary SA features can be implemented. Consequently, future research could focus on classifying SA data and information based on their purpose and level of criticality. Furthermore, our study excluded some systems present in the factory environment - such as warehouse management systems - which play a pivotal role in internal logistics. Future studies could explore a broader range of data sources and examine their role in developing mixed fleet SA, as well as their connection to the technical framework established in this study.

Notably, there are still no widely adopted, mature solutions that address all the information-sharing needs and practices identified by the project consortium partners. While information can be shared through a variety of user interfaces, this introduces a new challenge: carefully selecting and designing these interfaces to avoid overwhelming users with multiple systems, interfaces, and data sources. So far, this research has explored both the types of situation awareness (SA) information that can be technically produced and the differing information needs of two distinct user groups. Thus so far, our work has focused on the first three phases of the Human-Centred Design process - planning, understanding the context, and specifying user requirements (ISO, 2019). Future research could focus on generating design solutions and evaluating their effectiveness. It is clear that distinct UIs must be developed for various user roles, tailored to their specific information needs and operational requirements.

At the same time, fundamental usability principles (Nielsen, 1994) must be carefully applied to minimize cognitive load and reduce the risk of errors, which could impact both operational safety and worker wellbeing. The UI should deliver relevant information contextually, following the usability principle of "aesthetic and minimalist design". This necessitates identifying in future research, what information is required at each stage and situation of the work process. While essential information should be readily available, not all content needs to be visible on the main screen at all times. Additional details should be easily accessible without requiring significant effort from the user. Furthermore, the principle of "visibility of system status" must be upheld by clearly communicating system states, critical errors, and potential solutions or next steps. Lastly, the "recognition rather than recall" principle should guide UI design. This involves the consistent use of familiar UI elements - such as symbols, colours, text, and sounds - to aid user understanding and reduce the mental effort required to interpret the interface.

By selecting an appropriate set of situational awareness technologies and combining them with thoughtful, human factors-based UI design, optimal outcomes in mixed fleet optimization, safety, and user-friendliness can be achieved.

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