A comprehensive approach to safety for highly automated off-road machinery under Regulation 2023/1230
Open accessAbstract
As of January 2027, Machine Safety Regulation 2023/1230 replaces Directive 2006/42/EC, and a proposal for the regulation of Artificial Intelligence (AI) systems is soon to be ratified by the European Council. These legislative changes address hazards from increased automation and AI integration. Original Equipment Manufacturers (OEMs) face compliance challenges due to a lack of standardized guidelines, especially for off-road machinery with level 4 ‘high automation’ capabilities. At level 4 ‘high automation’, the machine operator assumes a supervisory role, intervening only in situations beyond the designed operational domain. Relying solely on supervisor intervention at this level of automation is unreasonable. Within this context, legislative safety requirements to maintain safety advocate for robust risk management systems, including both a human-in-the-loop safety option and adequate safety-related decision-making by the level 4 ‘high automation’ off-road machine, i.e., the safe reasoning software becomes paramount. In this research, conducted through a systematic mapping study, we discuss emerging safety trends and integration for the safe reasoning software a level 4 off-road machine requires. Our analyses shows the necessity of safety constraint-based design procedures. These procedures involve constraining the AI system by embedding fault management, reasoning checks, and diagnostic monitoring together with safety-related decision-making, thereby enhancing functional reliability and reducing risks. We consider these trends and patterns through the lens of changing machine safety legislative requirements, and the proposal of the regulation of AI products. Illustrating their implications alongside a case study of a level 4 tree harvesting machine, for a tangible and to industry transferable perspective on our results.
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Cites
Risk management in a dynamic society: a modelling problem
Rasmussen, J. (1997) · Safety Science · doi:10.1016/s0925-7535(97)00052-0 · Cited by 2,994 · see citing works
Guidelines for conducting systematic mapping studies in software engineering: An update
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.03.007 · Cited by 2,669 · see citing works
Engineering Psychology and Human Performance
Wickens, C. D., Helton, W. S., Hollands, J. G., & Banbury, S. P. (2021) · Routledge eBooks · doi:10.4324/9781003177616 · Cited by 686 · see citing works
The European Control Conference
(2013) · European Control Conference (ECC) · doi:10.23919/ecc.2013.6669086 · Cited by 558 · see citing works
2009 American Control Conference
(2009) · doi:10.1109/acc14809.2009 · Cited by 393 · see citing works
2016 IEEE International Conference on Mechatronics and Automation
(2016) · doi:10.1109/icma38789.2016 · Cited by 162 · see citing works
CaRINA Intelligent Robotic Car: Architectural design and applications
Fernandes, L. C., de Souza, J. R., Pessin, G., Shinzato, P. Y., Sales, D. O., Mendes, C. C. T., Prado, M. G., Klaser, R. L., Magalhães, A. C., Hata, A., et al. (2014) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2013.12.003 · Cited by 106 · see citing works
Integrated worker detection and tracking for the safe operation of construction machinery
Son, H., & Kim, C. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103670 · Cited by 105 · see citing works
Engineering problems in machine learning systems
Kuwajima, H., Yasuoka, H., & Nakae, T. (2020) · Machine Learning · doi:10.1007/s10994-020-05872-w · Cited by 84 · see citing works
Gil, M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107806 · Cited by 84 · see citing works
Combining Model Checking and Runtime Verification for Safe Robotics
Desai, A., Dreossi, T., & Seshia, S. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67531-2_11 · Cited by 84 · see citing works
Enhancing fault tolerance of autonomous mobile robots
Crestani, D., Godary-Dejean, K., & Lapierre, L. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.12.015 · Cited by 83 · see citing works
Evaluation Gaps in Machine Learning Practice
Hutchinson, B., Rostamzadeh, N., Greer, C., Heller, K., & Prabhakaran, V. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533233 · Cited by 75 · see citing works
Swuste, P., Groeneweg, J., van Gulijk, C., Zwaard, W., Lemkowitz, S. M., & Oostendorp, Y. (2020) · Safety Science · doi:10.1016/j.ssci.2019.104593 · Cited by 69 · see citing works
A Standard Driven Software Architecture for Fully Autonomous Vehicles
Serban, A. C., Poll, E., & Visser, J. (2018) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c.2018.00040 · Cited by 60 · see citing works
Loss of safety assessment and the IEC 61508 standard
Hokstad, P., & Corneliussen, K. (2003) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2003.09.017 · Cited by 60 · see citing works
2011 IEEE/AIAA 30th Digital Avionics Systems Conference
(2011) · doi:10.1109/dasc18226.2011 · Cited by 43 · see citing works
Grabbe, N., Kellnberger, A., Aydin, B., & Bengler, K. J. (2020) · Safety Science · doi:10.1016/j.ssci.2020.104665 · Cited by 39 · see citing works
Sensor Architecture and Task Classification for Agricultural Vehicles and Environments
Rovira-Más, F. (2010) · Sensors · doi:10.3390/s101211226 · Cited by 39 · see citing works
Recent Trends in Formal Validation and Verification of Autonomous Robots Software
Ingrand, F. (2019) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2019.00059 · Cited by 35 · see citing works
Safe and effective navigation of autonomous robots in hazardous environments
Seward, D., Pace, C., & Agate, R. (2006) · Autonomous Robots · doi:10.1007/s10514-006-9721-0 · Cited by 34 · see citing works
Verifiable Self-Certifying Autonomous Systems
Fisher, M., Collins, E. C., Dennis, L. A., Luckcuck, M., Webster, M., Jump, M., Page, V., Patchett, C., Dinmohammadi, F., Flynn, D., et al. (2018) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew.2018.00028 · Cited by 31 · see citing works
Supporting human supervision in autonomous collision avoidance through agent transparency
van de Merwe, K., Mallam, S., Nazir, S., & Engelhardtsen, Ø. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106329 · Cited by 28 · see citing works
Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification
Zaki, O. F., Dunnigan, M. W., Robu, V., & Flynn, D. (2021) · Robotics · doi:10.3390/robotics10010010 · Cited by 27 · see citing works
Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning
Ionescu, T. B. (2021) · Sensors · doi:10.3390/s21082589 · Cited by 27 · see citing works
Skill-based design of dependable robotic architectures
Albore, A., Doose, D., Grand, C., Guiochet, J., Lesire, C., & Manecy, A. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104318 · Cited by 26 · see citing works
Safety of industrial machinery in reduced risk conditions
Chinniah, Y. A., Aucourt, B., & Bourbonnière, R. (2016) · Safety Science · doi:10.1016/j.ssci.2016.12.002 · Cited by 25 · see citing works
A Safety Monitoring Model for a Faulty Mobile Robot
Leite, A. M. P. L., Pinto, A. M., & Matos, A. C. (2018) · Robotics · doi:10.3390/robotics7030032 · Cited by 25 · see citing works
Rule-based Dynamic Safety Monitoring for Mobile Robots
Adam, S., Larsen, M., Jensen, K., & Schultz, U. P. (2016) · Aisberg (University of Bergamo) · doi:10.6092/joser_2016_07_01_p120 · Cited by 22 · see citing works
Is Machine Learning Software Just Software: A Maintainability View
Mikkonen, T., Nurminen, J. K., Raatikainen, M., Fronza, I., Mäkitalo, N., & Männistö, T. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-65854-0_8 · Cited by 21 · see citing works
A hybrid control architecture for autonomous mobile robot navigation in unknown dynamic environment
Nakhaeinia, D., Payeur, P., Hong, T. S., & Karasfi, B. (2015) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2015.7294274 · Cited by 20 · see citing works
A Fail-safe Architecture for Automated Driving
Dorff, S. V., Böddeker, B., Kneissl, M., & Fränzle, M. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116283 · Cited by 20 · see citing works
Hybrid and Safe Control Architecture for Mobile Robot Navigation
Adouane, L. (2009) · HAL · Cited by 18 · see citing works
Auto GCAS for analog flight control system
Burns, A., Harper, D., Barfield, A., Whitcomb, S., & Jurusik, B. (2011) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc.2011.6096148 · Cited by 17 · see citing works
2023 9th International Conference on Automation, Robotics and Applications
(2023) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara56516.2023.10125910 · Cited by 17 · see citing works
A formal toolchain for offline and run-time verification of robotic systems
Zilio, S. D., Hladik, P.-E., Ingrand, F., & Mallet, A. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104301 · Cited by 15 · see citing works
A cognitive architecture safety design for safety critical systems
Dreany, H. H., & Roncace, R. A. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106555 · Cited by 12 · see citing works
A Safe Control Architecture Based on Robust Model Predictive Control for Autonomous Driving
Nezami, M., Nguyen, N. T., Männel, G., Abbas, H. S., & Schildbach, G. (2022) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc53348.2022.9867578 · Cited by 12 · see citing works
Behavior-Based Obstacle Detection in Off-Road Environments Considering Data Quality
Wolf, P., Ropertz, T., & Berns, K. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-11292-9_39 · Cited by 10 · see citing works
Validation of the impact of architectural flaws in six machine risk estimation tools
Chinniah, Y. A., Gauthier, F., Aucourt, B., & Burlet-Vienney, D. (2017) · Safety Science · doi:10.1016/j.ssci.2017.09.015 · Cited by 10 · see citing works
Towards a Standard Taxonomy for Levels of Automation in Heavy-Duty Mobile Machinery
Machado, T., Ahonen, A., & Ghabcheloo, R. (2021) · ASME/BATH Symposium on Fluid Power and Motion Control (FPMC) · doi:10.1115/fpmc2021-70251 · Cited by 9 · see citing works
Self-citationTriad concurrent approach among functional safety, cybersecurity and SOTIF
Kaneko, T., Yamashita, S., Takada, A., & Imai, M. (2023) · Journal of Space Safety Engineering · doi:10.1016/j.jsse.2023.09.001 · Cited by 9 · see citing works
Safety-oriented flexible design of Autonomous Mobile Robot systems
Desai, N., & Punnekkat, S. (2019) · IEEE International Symposium on Systems Engineering · doi:10.1109/isse46696.2019.8984481 · Cited by 8 · see citing works
A hierarchical fault tolerant architecture for an autonomous robot
Favier, A., Messioux, A., Guiochet, J., Fabre, J.-C., & Lesire, C. (2020) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w50199.2020.00031 · Cited by 8 · see citing works
Architecting Safer Autonomous Aviation SystemsPreprint
Fenn, J., Nicholson, M., Pai, G. J., & Wilkinson, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.08138 · Cited by 8 · see citing works
A Safe Control Architecture Based on a Model Predictive Control Supervisor for Autonomous Driving
Nezami, M., Männel, G., Abbas, H. S., & Schildbach, G. (2021) · European Control Conference (ECC) · doi:10.23919/ecc54610.2021.9655155 · Cited by 7 · see citing works
Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments
Dorff, S. V., Kneissl, M., & Fränzle, M. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9565022 · Cited by 7 · see citing works
Formally verifying navigation safety for ground robots
Zhang, M., & Zhang, X. (2016) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2016.7558699 · Cited by 5 · see citing works
Towards Situation-Aware Decision-Making for Automated Driving
Gerwien, M., Jungmann, A., & Voßwinkel, R. (2021) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara51699.2021.9376406 · Cited by 5 · see citing works
Wolf, P., Vierling, A., Husemann, J., Berns, K., & Decker, P. (2021) · Proceedings · doi:10.1007/978-3-658-29717-6_5 · Cited by 5 · see citing works
Safety Assessment: From Black-Box to White-Box
Kurzidem, I., Misik, A., Schleiß, P., & Burton, S. (2022) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew55968.2022.00083 · Cited by 4 · see citing works
Declarative Rule-based Safety for Robotic Perception Systems
Ingibergsson, J. T. M., Kraft, D., & Schultz, U. P. (2017) · University of Southern Denmark Research Portal (University of Southern Denmark) · Cited by 4 · see citing works
Autonomous safety decision-making in intelligent robotic systems in the uncertain environments
Agate, R., & Seward, D. (2008) · Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS) · doi:10.1109/nafips.2008.4531262 · Cited by 4 · see citing works
Müller, S., Wolf, P., Berns, K., & Liggesmeyer, P. (2018) · Proceedings · doi:10.1007/978-3-658-21300-8_26 · Cited by 3 · see citing works
Fault tolerant planning: towards dependable autonomous robotsPreprint
Lussier, B., Guiochet, J., Ingrand, F., Killijian, M., & Powell, D. (2015) · Cited by 3 · see citing works
Safe Autonomous Transport Vehicles in Heterogeneous Outdoor Environments
Toben, T., Eilers, S., Kuka, C., Schweigert, S., Winkelmann, H., & Ruehrup, S. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-34781-8_5 · Cited by 2 · see citing works
Automated Verification of Robot Software Models with Assume-Guarantee Reasoning in Isabelle/HOL
Yan, F., Ballenghien, B., Foster, S., Cavalcanti, A., Baxter, J., & Wolff, B. (2026) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2026.10 · Cited by 2 · see citing works
Multi-level fault tolerance for autonomous robots - Application to an underwater robot
Hereau, A. (2022) · theses.fr (ABES) · doi:10.70675/69dcc82dzc31dz498bz82feze6587627c994 · Cited by 2 · see citing works
Chandrasekaran, B., & Ruiz, A. Y. R. (2021) · International Conference on Mechatronics, Robotics and Automation (ICMRA) · doi:10.1109/icmra53481.2021.9675591 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 66 references couldn’t be matched to a known paper.
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Sorted by citations
Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery
Wu, H., Wang, X., Chen, X., Zhang, Y., & Zhang, Y. (2025) · Agriculture · doi:10.3390/agriculture15121297 · Cited by 54 · see citing works
de Oliveira, C. S., Toledo, R. D. S., Victorio, V. H. T. O., & von Wangenheim, A. (2025) · IEEE Access · doi:10.1109/access.2025.3551453 · Cited by 15 · see citing works
Applications of robotics and extended reality in agriculture: A review
Anastasiou, E., Ntakos, G., Kanakari, E., Bitsika, S., Gemtou, M., Katsaragakis, M., Soudris, D., Volioti, C., Arvanitou, E.-M., Folina, M.-T., et al. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101521 · Cited by 15 · see citing works
Machinery safety improvement in manufacturing-oriented facilities: a strategic framework
Li, H., Sun, Y. X., Adumene, S., Goleiji, E., & Yazdi, M. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15763-w · Cited by 9 · see citing works
Process safety in the era of AI: Assurance, deployment, and measurable risk reduction
Christopher Selvam, D., Raja, T., Mehar, K., Devarajan, Y., Shwetha, A., Sunil Kumar, M., Tiwari, A., Gill, A., & Nayak, P. P. (2026) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2026.105956 · Cited by 9 · see citing works
Klüver, C., Greisbach, A., Kindermann, M., & Püttmann, B. (2024) · Security and Safety · doi:10.1051/sands/2024020 · Cited by 8 · see citing works
“Even if the same AI method is used, it must be considered that the standards in the area of e.g. , aerospace are different from those for automated driving or mining.”
Predicting road traffic accident severity from imbalanced data using VAE attention and GCN
Shangguan, A., Feng, N., Hei, X., Fei, R., Jin, Y., Mu, L., & Li, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-17064-4 · Cited by 5 · see citing works
Jayabal, R., Subbiah, G., Tripathy, S., Nagendra, S. V. H., Kalidhas, A. M., Vadnagra, K. V., Sandeep, G. M., Smerat, A., & Kamakshi Priya, K. (2026) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2026.106026 · Cited by 5 · see citing works
Dynamic and probabilistic safety zones for autonomous mobile robots operating near humans
Saffre, F., Hildmann, H., Heikkilä, E., Malm, T., & Pakkala, D. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102731 · Cited by 5 · see citing works
Enablers and barriers to AI adoption: evidence from the heavy machinery industry
Valtonen, A., Kokkonen, K., Saunila, M., Verdoja, F., Orzechowski, G., Kurvinen, E., Aaltonen, P. H. M., & Salakka, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01038-0 · Cited by 4 · see citing works
Chen, Z., Zhang, L., Chu, J., Fang, X., & Dui, H. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-025-4173-4 · Cited by 3 · see citing works
Construction Site Equipment: Moving Towards the New Machinery Regulation
Cet, G. D., Temporin, D., Paparella, R., Vianello, C., Baraldo, L., & Boso, D. P. (2026) · Buildings · doi:10.3390/buildings16071391 · Cited by 1 · see citing works
de Koning, M., Minav, T. A., & Ghabcheloo, R. (2025) · Safety Science · doi:10.1016/j.ssci.2025.106883
Self-citationDevarajan, Y., Raja, T., Saxena, D. N., Devakki, B., Jena, S. P., Yadav, N. K., Kalidhas, A. M., Jayabal, R., & Mehar, K. (2026) · Digital Chemical Engineering · doi:10.1016/j.dche.2026.100339
The Machinery Regulation (EU) 2023/1230 in the Development of Operational Safety Assurance
Górny, A., & Jasiulewicz–Kaczmarek, M. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-39562-7_26
- Authors
- de Koning, M., Machado, T., Ahonen, A., Strokina, N., Dianatfar, M., De Rosa, F., Minav, T., & Ghabcheloo, R.
- Affiliations
- Tampere University (FI)
- ORCID
- Venue
- Safety Science
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 175
- Pages
- 106517
- ISSN
- 0925-7535 (ISSN-L)
- OpenAlex
- W4393302582
- Cited by
- 17
- References
- 66
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 2
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Safety Science (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
- Funders
- Tampere University
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- off-road machinery, high automation, Level 4 automation, AI regulation, constraint-based design, fault management, diagnostic monitoring, risk management
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