Increased sensing and computing capabilities in cars are crucial for advanced trafic and driving automation. However, novel data delivery, testing, and machine learning pipelines are still needed to harness the full capabilities of automotive sensing solutions. At the same time, vehicular digital twins are needed to enable versatile testing and simulation capabilities. This paper depicts the Vehicle-In-The-Loop (VIL) cloud interface and verifies data consistency regardless of the source. The study aims to determine how data collected from simulation corresponds to real test drive data. The data is collected from both simulation and actual test drives. Utilising the MQTT protocol, data is stored on a cloud server and further fed into Unreal Engine 5, where the test drive is replayed, and its correspondence to the real drive is ensured. This work ofers a new perspective on verifying data consistency between simulated and real test drives and complements the vehicle abstraction opportunities provided by Eclipse KUKSA. Our results highlight digital twin creation as a part of automotive software development and set premises for testing and validating complex use cases, such as trafic accidents and extreme weather, that can rarely or only with severe expenses be tested in real-life situations.
CEUR
ceur-ws.org © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License test drive in a real car. For validation, we determine how
ing capabilities that are crucial for advanced trafic and driving automation, applications spanning from safety Today’s cars hold considerable computational and sens- these digital models dynamically based on measurement is broad, and definitions may vary. Still, the main idea is to model physical systems with digital means and update data. In essence, digital twin methods provide digital spaces where reality can be modelled virtually [7] as it livery and management protocols and interfaces that are features to fully automated vehicles. However, data de- is or would be in unseen but possible situations. Indeed, ily closed in company-specific silos [ 1]. The first eforts required for machine learning pipelines are still primar- software development sets premises for testing and valithe creation of the digital twin as part of automotive dating complex use cases, such as trafic accidents and terfaces from the car to the cloud environment include for creating open-source data transfer protocols and in- extreme weather, that can rarely or only be tested in realEclipse Kuksa [2], of which this work also bases, but con- digital twins for automotive software development opens siderable work is still required for data validation and benchmark eficiency of the proposed frameworks. Data sharing through open interfaces can boost innovation by more eficient and accurate machine learning models that cover more expansive geographic areas and use cases [3].
nEvelop-O
0009-0006-4132-9496 (O. Timonen); 0009-0004-3192-7711 (A. B. Z. Mahmoodi); 0000-0002-3374-671X (E. Peltonen) life situations with severe expenses. The utilisation of avenues for testing diferent sensors and components in actual use cases, such as studying the longevity of such components and proposing novel learning strategies that combine multiple data sources.
This paper describes the Vehicle-In-The-Loop (VIL) cloud interface. It verifies data consistency regardless of the source: a real car on the road or a virtual object in platform is provided in Figure 1. We use KUKSA.val [8] that provides a vehicle abstraction layer to enable the management and use of vehicle signals. As a dig5. Capabilities of utilising such a game engine in digital twin creation have been successfully demonstrated in wind power plants [9] and cultural tourism [10]. Using a game engine and a VIL cloud interface enables visual simulation that complements the capabilities provided by KUKSA.val; KUKSA.val is used to collect data from a tions in industry, energy, and transportation verticals [4]. the digital twin environment. The overview of the Kuura 11.6.2024 Vaasa, Finland TKTP 2024: Annual Doctoral Symposium of Computer Science, 10.- ital twin modelling framework, we use Unreal Engine data collected from the simulation corresponds to real edge and cloud back-ends can perform challenging infertest drive data in a real-life driving scenario. Utilising ence and learning tasks to support drivers’ cognition and the MQTT protocol, data is stored on a cloud server and automate the driving scenario [11, 12]. Such intelligent further fed into Unreal Engine 5, where the test drive systems demand training data, which in-vehicle sensors is replayed, and its correspondence to the real drive is and external databases can provide. How to make the ensured. This work ofers a new perspective on verifying data available, processed, and utilised in a challenging data consistency between simulated and real test drives real-time and mobile environment is a timely research and complements the vehicle abstraction opportunities question. provided by Eclipse KUKSA. Vehicular safety systems (and any other relevant ap
The main contributions of this work are the following: plications) of any level of driving autonomy require data 1) We provide the Kuura platform for similarly collecting from in-vehicle sensors [13], such as cameras, LiDARs, vehicular sensor data to a real car and similar test runs radars, and speed meters [14, 15]. This information can in a digital twin environment. With this, we extend the be used to, for example, improve lane [16] and road potKUKSA.val environment to better fit digital testing and hole [17] recognition. Solutions for detecting drivers’ validation tasks. 2) We explore Unreal Engine 5 as a ve- behaviour while using smartphones during driving [18] hicular digital twin environment and provide a pipeline and drunk driving [19] have been explored. However, to deploy such digital twins with simulated and real test the results underline that human drivers’ perception and drives. 3) We experiment with the data consistency be- reasoning still maintain an advantage compared to fully tween simulated and real test drives and further demon- automatic vehicles [20]. strate the power of game engine-based digital twins in However, most of the in-vehicular and driver’s pervehicular computing and sensing scenarios. sonal sensors and interfaces are brand-specific or closed, limiting access to the data, computing, and networking capabilities and thus hindering vehicular application de2. Background velopment. To enable connected vehicles to utilise all the available data sources, AI/ML computing resources, 2.1. Vehicle as a Sensing Device and networking capabilities, open-sourced general inModern cars implement technologies for automatic brak- terfaces and software platforms need to be defined [ 1]. ing, Cooperative Adaptive Cruise Control (CACC), pre- On-board diagnostics (OBD) protocol refers to a vehivention of unwanted lane crossing, distance keeping, and cle’s self-diagnostic and reporting capability. The more so on, to supply drivers’ own cognition and prevent acci- advanced OBD-II is a protocol homogenised into the vehidents. For further technological advancement, vehicles cle itself, allowing software-defined onboard operations will require artificial intelligence (AI) and machine learn- and, most importantly, collecting a wide range of vehicing (ML) capabilities depending on efective data transfer ular data to the software-defined vehicle’s case. This and management systems. With increased networking includes but is not limited to engine load, coolant temand computing capabilities, vehicles and their supporting perature, fuel pressure, engine revolutions per minute (RPM), vehicle speed, intake air temperature, airflow rate, throttle position and many types of sensor data like oxy- precisely under source made available license, making gen sensors and fuel system status. OBD-II has relatively it suitable for various applications in autonomous and easy access to the mentioned sensors, which is enough to driving-support test cases [22]. The key diferences beprove the concept. In the future, research conducted with tween Unity and Unreal Engine are summarised in Table vehicular sensors can utilise direct access to the vehicle’s 1. Unreal Engine has typically been considered a better controller area network (i.e. CAN bus) and standardised choice for 3D games, while Unity has been considered a architectures such as AUTOSAR for wider data access. strong choice for 2D games.
The previous literature emphasises the importance 2.2. Automotive Simulations of determinism in simulation environments to ensure repeatability, allowing for trustworthy and easily debuggable results. Game engines still may come with challenges of non-deterministic behaviours. For example, the investigation by Chance et al. [24] reveals significant simulation variance in CARLA, particularly due to actor collisions and system-level resource utilisation. As such, accuracy investigation is one of the key goals in our preliminary work presented in this paper.
Driving and trafic simulators are used in the automotive industry as an alternative to costly and potentially dangerous real-life testing [21]. The advantages of such practices highlight efectiveness in analysing human driving behaviour and essential trafic situations often too hazardous to test in real-life scenarios, such as extreme weather, congestion, and accidents [22]. This can be especially emphasised in the increasing reliance on simulation technologies for assessing human driving factors. 2.3. Automotive Digital Twins The more real-life, photo-realistic simulations enable simultaneous testing of vehicle dynamics and stochastic Digital twins (DT) in the context of vehicles is an emergpedestrian, driver, and vehicle interactions in various ing field that has attracted significant attention in both scenarios [23]. industry and academia [27]. Digital twins are virtual
However, traditional simulators often have limitations representations of physical entities, such as vehicles, that in emulating real-life behaviour and perception. This has aim to mirror the lives and behaviours of their real-world led to a growing interest in game engines as simulation counterparts [28]. These digital replicas use the best platforms for developing and testing autonomous vehi- available physical models, sensor updates, and other data cle control systems [24]. Several vehicular simulators, sources to simulate and predict the behaviour of the corsuch as CARLA, AirSim and CarSIM, provide simula- responding physical twin [29, 30]. One area where digital tion capabilities and environments to support vehicle twins have shown great potential is in the automotive research and development. These platforms have been industry, particularly for electric vehicles [31]. Digital used to study vehicle autonomy, safety and performance. twins can greatly benefit electric vehicles, which have CARLA is a free open-source simulator to support au- gained greater market share in recent years. By creating tonomous vehicle systems’ development, training, and a digital twin of an electric vehicle, manufacturers and validation. AirSim is a simulator for drones and cars researchers can simulate and optimise its performance, developed by Microsoft. It can also provide the possibil- energy consumption and other key parameters. Unlike ity to experiment with deep learning, computer vision traditional simulators, digital twins provide beyond capaand reinforcement learning algorithms in autonomous bilities for human-machine interaction and performing vehicles and the creation of complex and changeable envi- data-driven actions in real-world scenarios. ronments and additional sensor modalities [25]. CarSim Digital twins also play a key role in the design and is a vehicle dynamics simulation platform that allows the development of autonomous vehicles [32]. The concept simulation of vehicle behaviour in diferent conditions of digital twins is closely related to the transition to dataand environments, including motor dynamics, through driven vehicles, as it enables the analysis and validation Simulink models. It can be used to create accurate models of autonomous vehicle designs [33]. By exploiting digital of vehicles and simulate their behaviour under diferent twin technologies, researchers can assess the safety and road surfaces, weather conditions, and trafic situations security of autonomous vehicles and identify potential [26], but is not open-sourced. risks and vulnerabilities. Furthermore, combining digi
In this study, we use Unreal Engine, renowned for its tal twins with combined vehicle technology and cloud versatility, high-quality graphics and realistic physics computing has led to the development of the Mobility simulation, which is useful for simulating vehicles [21]. Digital Twin (MDT) framework [34]. These frameworks Competing game engines include Unity and CryEngine, consist of digital representations of people, vehicles, and of which CryEngine is the smaller project. The main transport, which enable the analysis and optimisation arguments that favour Unreal Engine are it is free of of mobility and large-scale trafic systems. By exploitcost for research and commercial projects until making ing real-time data and simulations, MDT frameworks one million revenue, has open source code even it is can support decision-making processes and improve the
Unreal Engine
Unity
2.4. Open-sourced Automotive Software eficiency and safety of transportation systems. abilities. The maintenance and support of open-source
Digital twins enable the simulation, optimisation, and software can be uncertain if their developers and comanalysis of vehicle performance, energy consumption, munity are not active or committed. While open-source and safety and security. Combining digital twins with software is dynamic and constantly changing, vehicles connected vehicle technology and cloud computing will purchased today will remain in trafic for decades. In extend their capabilities to optimise mobility systems. addition, there is a need for precise quality control and As technology advances, digital twins can be expected software certification in the automotive industry, which to play a key role in shaping the future of vehicles and can be challenging to implement in an open-source entransport systems. As such, current technologies aim to vironment because access to representative designs and create models for distributed multi-agent cyber-physical industry-standard methodologies is limited. This limisystems using co-simulation [35]. Such large-scale digi- tation challenges researchers as automotive companies tal twins should be able to make predictions about the do not openly share their development life-cycles and future condition and behaviour of the vehicle [36]. How- verification methods, each maintaining proprietary techever, AI-based digital twin capabilities require data co- niques. Given this scenario, there is a growing demand operation and load-balancing, scheduling, and network for open-source solutions to support the development security schemes over vehicle-to-cloud computing con- and research of automotive applications, emphasizing tinuum [37]. the need for open-source benchmarks to facilitate research across various aspects of automotive application development. [41].
Open source software refers to software that has a publicly available and editable source code. This allows col- 3. Kuura Implementation laborative development and innovation. One of the most remarkable benefits of open-source software is its flexi- 3.1. Design Principles bility and customizability, as user communities can adapt The cornerstone of our framework is grounded in the the software to their specific needs. Open source is also principle of open-source development, ensuring transcost-efective as it is free and reduces dependencies on parency and collaborative potential. Simplicity is at the specific software providers. The use of open source also core, paving the way for efortless future evolution. Our ofers opportunities for innovation in automotive soft- design philosophy revolves around creating a system that ware development and promotes the use of new tech- is not just functional today but remains adaptable and nologies and solutions [38, 39]. maintainable for tomorrow’s innovations. The essence
For the automotive industry, open-source software of this framework is to avoid complexity instead of empresents some unique challenges as vehicular software, bracing a minimalist approach that prioritises ease of by default, has life-critical safety and reliability require- understanding and operation. One must consider the ments [40]. Technically, anyone can modify the source life cycle of software components, as updates and decode, which may create unwanted surprises and vulner- pendencies are inevitable. The framework architecture is designed to handle these, avoiding obsolescence and limited devices or low-bandwidth networks. incompatibility. The Kuura framework presents a cohesive suite of components, each selected for robustness and simplicity. 3.2. Kuura Architecture Design At its foundation lies the integration of Kuksa, SMAD, and Kuura, delineating a timeline of iterative progress The general architecture of Kuura is shown in Figure 2. as detailed in Table 2. Each iteration is a response to the We chose the Unreal Engine 5 game engine because of evolving needs and challenges encountered. Kuksa, iniits versatility in creating realistic simulations. This is tially misaligned with its focus on automotive app stores an essential part of the research objective to verify the and firmware updates, has since been archived [ 8, 2]. consistency of the data between the simulation and the SMAD was unsustainable due to its complexity and poor real test runs. The MQTT protocol was chosen to collect documentation [42]. Our simplified stack emerges as and transfer the data to the cloud server and the game a response, stripping away the superfluous to focus on engine, as it is reliable and eficient for real-time data functionality. It leverages OpenShift (run on CSC Rahti transfer, which might be the next step in the research container cloud 1) for its cost-eficiency compared to Miand, thus, critical requirements in our study. The MQTT crosoft Azure. This pragmatic approach is engineered protocol operates asynchronously and is considered an to reduce complexity, cost, and maintenance overhead, ideal choice for IoT applications that often operate on Purpose
SMAD stack
Kuura (this paper)
streamlining operations without compromising capabil- car. A laptop computer running Linux was connected to ity. the adapter, and a script was run to record data from the
Each framework iteration — Kuksa, SMAD, and Kuura vehicle in a log file. The successful log file collection was — brings new insights. Kuksa’s archival signals a further important in developing the auto-client script pivot away from its original automotive-centric fo- for future larger tests and ensuring the whole system’s cus. SMAD’s downfall was its complexity and reliance functionality. Practical testing in the first phase was on now-inaccessible Kubernetes Helm charts. Kuura carried out by driving the car and ensuring the data was emerges as the distilled essence of its predecessors, em- stored correctly and its format was manageable. bodying simplicity and sustainability. By eliminating non-essential components, Kuura adapts existing func- 3.4. Data Transmission tionalities with more straightforward tools, significantly reducing cost and complexity and enabling an environment conducive to continuous development and operation.
we want to enable multiple clients to listen to the generated data simultaneously. One example of such a scenario is live visualisation of the data while saving it to a 3.3. Vehicle Data Reader database without additional latency. While we could also save the data and then fetch it from the database, this The OBD-II is a port designed for diagnostic purposes. would add latency to the visualisation. MQTT also has It has multiple buses available. These buses include the built-in, easy-to-configure security mechanisms. Setting CAN bus, SAE-1850 and ISO-9141-2. The automotive up MQTT with SSL is very easy, and configuring the manufacturers can also provide other networks at their MQTT broker to require client certificates for communidiscretion [43]. The bus we are most interested in is cation is also very easy. The connection can also be set the CAN bus. On some vehicles, the CAN bus available up to require a username and password. at the OBD connector can be protected by a gateway We could also use HTTP or raw TCP/UDP sockets as device restricting access to some data from the OBD port. an alternative for MQTT. While HTTP ofers security Unlike the CAN bus inside the car, you must poll the measures similar to MQTT, it does not have multi-cast OBD port to receive any data. While we could get most by default. While it is not hard to implement, MQTT has of the data we wanted from the OBD port, some data, it built in and is most likely already done correctly. One like the steering wheel position, was unavailable. This advantage HTTP has over MQTT is the ability to commakes the OBD port unsuitable as a data source for our municate directly between two applications, eliminating purposes, as it would make it quite dificult to drive the the need for a broker in cases where there is only one virtual car in Unreal accurately. client.
In the evaluation phase, we collected data from an Raw sockets are the most basic option, and they don’t OBD-II Bluetooth adapter connected to a Toyota RAV4 come with any of the advanced features included in
MQTT out of the box. However, they are very versa- 3.5. Cloud Environment tile and can be used for various purposes. One advantage the sockets would ofer is the ability to write raw The cloud environment receives data from the MQTT can data as is to the socket. This would enable saving broker. The environment also has a Python script that raw can dumps in a database with minimal overhead connects to the broker to receive the data from the vehicle. if we ever needed/wanted to support it. One problem The script stores all of the messages received by InfluxDB. with multi-cast solutions is that the provider has no idea The point name and field are derived from the MQTT if any clients are listening for the sent data unless the topic. The timestamp is also gotten from the MQTT clients have been programmed to provide feedback when message payload. Since the timestamp is included in the they are listening. This makes it harder to implement the message, we could use any database solution to store the provider in a way that it holds the messages in memory data. If the timestamp were missing, however, then a or saves them locally in case the data is sent to nowhere. time series database would be our only option since the
In the experiments, a laptop was used as the in-vehicle message times are crucial for playback at a later time. client running Ubuntu 22.04 LTS, and the script collect- By getting the message time from the provider, we can ing the data was written in Python using an OBD library ensure that network conditions do not afect the accuracy [44]. The script writes read values into a CSV file locally of the recorded timestamps. and publishes them using the MQTT protocol. The back- InfluxDB, a time series database used to store large end was deployed on CSC’s Rahti as RedHat OpenShift amounts of time-stamped data due to its high perfordeployments. On the server side, Mosquitto MQTT bro- mance and scalability, was stored at the onset of the proker forwards the published messages to subscribers. The cess. Storage is essential in handling large amounts of most important subscriber is a Python service that stores data that emanate from driving vehicles. A Python script received messages in an InfluxDB instance. As an addi- was then used in the next stage of the data-processing tional demonstration, Grafana was deployed to provide workflow. This script had two main functionalities: First, a real-time dashboard for the published and stored data. it reads GPS point data pre-recorded into a JSON file, The sequence diagram is provided in Figure 3. which is vital in mapping out routes of vehicles. Secondly, this script establishes a connection with InfluxDB to retrieve useful information within a particular range.
This recovery is critical for evaluating the vehicle’s performance and environmental conditions during various experiment stages.
At this point, the processed data goes through an MQTT broker using a Python script. Once more, this protocol provides lightweight messaging, providing fast and reliable real-time information transmission that would be needed for the simulation environment. 3.6. Simulation Environment Multiple reasons contributed to the choice of Unreal Engine 5 game engine, including the capacity to create realistic simulations of real car driving and the possibility of driving a car in a simulation, thereby generating corresponding data. The research aimed to ensure uniformity between the simulation and actual driving, thus requiring realistic simulations. Unreal Engine 5 is also open-source, which meets one of the implementation principles of the study, making future development as easy as possible.
The research utilised the MQTT protocol, one of the key IoT connections and data collection components. Unreal Engine does not have native MQTT support. For this reason, we used the NinevaStudios MQTT-utilities extension with some modifications. This extension allowed MQTT data communication, which is essential for collecting data from the simulation, with minor adjustments made to transfer it to cloud storage securely. Through this connection, it was possible to develop a dynamic and interactive simulation environment.
Lastly, we simulate a car running along received GPS points as shown in Figure 4. In the simulation, the vehicle’s movement was driven by speed data acquired from the MQTT broker. As a result, real-time synchronisation between the GPS points and speed data gave an actual representation of the journey made by the vehicle, hence allowing for the immersion of details about its performance in diferent circumstances. Such a holistic approach to data storage, processing, transmission, and visualisations shows how diverse technologies can be integrated into high-level vehicular data analysis and simulation.
The initial version of the Kuura presented in this paper has a dynamic road generated as the car moves around, thus simplifying testing by making it independent of environmental conditions. This method enables better lfexibility in the testing process because it does not require a predefined route or special environmental circumstances. Generating dynamic roads is essential to ensure the reliability of the data collection system. This phase, built on the multiple approaches used in the study, emphasises adaptability and precision. By generating the road during runs accuracy of collected data could be instantly evaluated. It is particularly advantageous to work within this dynamic environment for the purposes of identifying and solving prospective issues within a workflow for data processing that would make it strong and eficient. This technique also makes Unreal Engine simulation more elaborate. It allows diferent scenarios to be run on a platform without sticking to a single static map, giving the evaluation process more flexibility.
4.1. Real-life Experiment
vehicle testing area using a Toyota RAV4 Hybrid 2019 vehicle. A closed area, such as OuluZone, was chosen because it allows for assessing the drives and their safety. The significance of this place is that it helped gather and analyse information in real-life scenarios, thus allowing comparison and verification with data collected from virtual and actual driving instances. Besides being a recreational driving and sports centre, OuluZone is also a notable site for research and learning, especially on autonomous cars and related technologies.
Several laps were driven during the tests, some with the cruise control set at diferent speeds (30km/h, 40km/h, and 50km/h) to facilitate the validation of results in the simulation with data collected at a constant speed. Laps were also driven without cruise control at varying speeds. Driving data was collected during the test via an OBD-II Bluetooth adapter connected to a laptop running Linux. This allowed for the vehicle data to be logged and its format managed. Towards the end of the tests, a USB adapter enhanced data collection. 4.2. Virtual Experiment
drive test drive scenarios comparable to our real-world data collection eforts. In this experiment, we used the same logger used during actual test drives with a real car, ensuring a uniform approach to data acquisition and proving that the logger could be used without changes in both 5. Discussion and Conclusions environments. We gathered data on speed and time from the virtual test drive, which can be cross-verified with the real car’s outputs. The current limitation of real-world data collection stems from the OBD-II interface’s inability to provide comprehensive vehicle diagnostics. In the virtual setting, we collected additional data such as gear, throttle, brake application, and steering angle. These were predominantly included for illustrative purposes, aiming to demonstrate the extensive data collection possibilities within a simulated environment. It is important to note that verifying these additional parameters will become feasible with future access to the CAN bus, allowing for a more detailed and accurate comparison between virtual and real vehicle data.
In this study, we have aimed to bring new insights into vehicular data collection and the creation of digital twins by using the Eclipse Kuksa platform and Unreal Engine 5 to simulate driving scenarios. Our main focus was providing an overview of the simplified vehicular data collection architecture that can be easily developed for further projects and verifying the consistency between real and simulated vehicular data through practical realworld experimentation.
Using the MQTT protocol for sending data and Unreal Engine 5 for simulation has allowed us to compare real driving data with simulated ones. This method makes 4.3. Experimentation Results digital twins more reliable and allows later use for testing In our validation process, we specifically focused on com- in many conditions that are hard or expensive to create paring the collected GPS data and speed data between the in real life, like very bad weather or diferent kinds of actual and virtual driving tests conducted in Unreal En- trafic situations. gine 5. As shown in Figure 5, the same InfluxDB database We encountered challenges in data collection via the successfully contains both real-world data (smad/toyota) OBD-II protocol because it is filtered and does not allow and virtually collected data (unreal/toyota). This design the collection of all possible data. This limitation highwill further allow simultaneous analysis of both virtual lighted the need for more comprehensive data acquisition and real-world data sets, allowing us to expand the digital methods like the CAN bus. The data collection limitatwin creation capabilities with virtual realities and actual tions prompted us to consider future enhancements in real-life test runs, independently of the data source. our methodology to achieve a more accurate and encom
As illustrated in Figure 6, we successfully mapped the passing digital representation of the vehicle. collected GPS data onto the 3D model of the racetrack in Our findings open up possibilities for future research runtime from cloud and verified its accuracy. This demon- directions, including optimising data transmission methstrates that our virtual environment can accurately repli- ods for improved eficiency and exploring bi-directional cate real driving conditions. The speed data collected in data flow between the digital twin and the vehicle. Such the database corresponded with the data obtained in the advancements could potentially enable real-time vehicle Unreal Engine 5 simulation, confirming the consistency control based on digital twin data. of data in both real and virtual driving scenarios. While By integrating additional simulation models and conthe data transmitted from the game engine to the server sidering more sophisticated data collection interfaces, we was also accurate, at this stage, our primary focus was anticipate that future iterations of this work will address on verifying the accuracy of speed and time information. the current limitations and unlock new capabilities for Expanding this experimentation to cover a wider range digital twins in automotive research and development. of variables is possible in future research. The potential for these technologies to improve vehicle safety, eficiency, and innovation is immense, paving the
ZON project CHIPS-JU CIA FEDERATE (grant number 101139749), Business Finland project 6G Visible (grant number 10743/31/2022), and the Finnish Research Council project Northern Utility Vehicle Laboratory Consortium GO!-RI (grant number 352726). way for a more interconnected and intelligent transportation ecosystem.
Future eforts should be made using the CAN bus instead of the OBD-II to improve accuracy completeness and to have access to all possible data the vehicle provides. Reconsidering data transmission methods, like MQTT, for more eficient data multicasting is also a possible future direction. In the future, we are also looking into sending data from the game engine to the car instead of just storing it in the cloud, having the car drive in real life and the game engine simultaneously with as little latency as possible and importing Eclipse Arrowhead to extend possibilities with simulation models, such as using the architecture with Matlab Simulink or corresponding open-sourced physics modelling software. [20] B. Schoettle, Sensor fusion: A comparison of sens- security in autonomous vehicles, IEEE Communiing capabilities of human drivers and highly auto- cations Standards Magazine 5 (2021) 40–46. mated vehicles, University of Michigan (2017). [33] T. Fuchs, M. Zinser, K. Renatus, B. Bäker, Data [21] D. Michalík, M. Jirgl, J. Arm, P. Fiedler, Developing model of automotive digital twins, ATZelectronics an unreal engine 4-based vehicle driving simulator worldwide 16 (2021) 52–57. applicable in driver behavior analysis—a technical [34] Z. Wang, R. Gupta, K. Han, H. Wang, A. Ganperspective, Safety 7 (2021) 25. lath, N. Ammar, P. Tiwari, Mobility digital twin: [22] S. Malik, M. A. Khan, H. El-Sayed, Carla: Car learn- Concept, architecture, case study, and future chaling to act — an inside out, Procedia Computer lenges, IEEE Internet of Things Journal 9 (2022) Science 198 (2022) 742–749. 12th International Con- 17452–17467. ference on Emerging Ubiquitous Systems and Per- [35] M. Palmieri, C. Quadri, A. Fagiolini, C. Bernardevasive Networks / 11th International Conference schi, Co-simulated digital twin on the network edge: on Current and Future Trends of Information and A vehicle platoon, Computer Communications 212 Communication Technologies in Healthcare. (2023) 35–47. [23] A. Dubs, V. C. Andrade, M. Ellis, S. Ganley, B. Kara- [36] G. Bhatti, H. Mohan, R. R. Singh, Towards the future man, O. Toker, A photo-realistic simulation and test of smart electric vehicles: Digital twin technology, platform for autonomous vehicles research (????). Renewable and Sustainable Energy Reviews 141 [24] G. Chance, A. Ghobrial, K. McAreavey, S. Lemaig- (2021) 110801.
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