# Engineering Reliable Autonomous Systems: Challenges and Solutions

arXiv: https://arxiv.org/abs/2606.23760 · HTML: https://mlsanity.com/paper/2606.23760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 87 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Algorithms for Verifying Deep Neural Networks](https://mlsanity.com/paper/1903.06758/) (2019)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)](https://mlsanity.com/paper/2301.05815/) (2023)
- [Deep Learning with Logical Constraints](https://mlsanity.com/paper/2205.00523/) (2022)
- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges](https://mlsanity.com/paper/2412.04782/) (2024)
- [A Corroborative Approach to Verification and Validation of Human--Robot Teams](https://mlsanity.com/paper/1608.07403/) (2016)
- [Specification, Validation and Verification of Social, Legal, Ethical, Empathetic and Cultural Requirements for Autonomous Agents](https://mlsanity.com/paper/2307.03697/) (2023)
- [Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic](https://mlsanity.com/paper/2206.14372/) (2022)
- [Heterogeneous Verification of an Autonomous Curiosity Rover](https://mlsanity.com/paper/2007.10045/) (2020)
- [Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning](https://mlsanity.com/paper/2103.10847/) (2021)
- [Autonomous Systems' Safety Cases for use in UK Nuclear Environments](https://mlsanity.com/paper/2310.02344/) (2023)
- [Robotics and Integrated Formal Methods: Necessity meets Opportunity](https://mlsanity.com/paper/1805.11996/) (2018)
- [Towards Compositional Verification for Modular Robotic Systems](https://mlsanity.com/paper/2012.01648/) (2020)
- [SUAVE: An Exemplar for Self-Adaptive Underwater Vehicles](https://mlsanity.com/paper/2303.09220/) (2023)
- [Modular Verification of Autonomous Space Robotics](https://mlsanity.com/paper/1908.10738/) (2019)
- [Leveraging Compositional Methods for Modeling and Verification of an Autonomous Taxi System](https://mlsanity.com/paper/2304.13517/) (2023)
- [Multi-Robot Task Planning to Secure Human Group Progress](https://mlsanity.com/paper/2310.07731/) (2023)

## Abstract (arXiv)

Engineering reliable autonomous systems is an important and growing topic in computer science. As autonomous systems become more prevalent, easy-to-use techniques for building them reliably are increasingly important. This workshop report captures and expands on the discussions at the Lorentz Center Workshop "Engineering Reliable Autonomous Systems" (ERAS), held from 10 to 14 June 2024. The workshop was co-organised by the organisers of the Workshop on Formal Methods for Autonomous Systems (FMAS) and the Workshop on Agents and Robots for reliable Engineered Autonomy (AREA). It brought together members of the FMAS and AREA communities, industry practitioners, and representatives from sectors where autonomous systems pose distinctive engineering challenges. The workshop focused on three main research topics: techniques for verification and validation of autonomous systems; engineering real-world autonomous systems; and software architectures for safe autonomous systems. Its main outcome is a catalogue of challenges in these areas and, most importantly, a pathway to solutions. Some challenges can already be tackled by techniques that are well known in academia but have not yet become regularly used in practice. Other challenges remain unresolved and require further research. This roadmap is intended to support future research and industrial collaboration.