Posts Tagged ‘Artificial Intelligence’

New paper about software verification and validation of safe autonomous cars published on IEEE Access journal

Thursday, January 21st, 2021

We are glad to announce this new paper published on a top IEEE open access journal (Impact Factor:  3.7, Scimago Journal Rank: Q1 Computer Science).

The paper is co-authored by Nijat Rajabli, Francesco Flammini, Roberto Nardone and Valeria Vittorini and it is a literature review on the software Verification and Validation (V&V) of safe autonomous cars. The paper is a result of the outstanding job done by Nijat, a Linnaeus University student, during his project work for the course of Current Topics in Computer Science.

We believe that due to its quite extensive topic coverage, the paper can serve as a useful compendium for the many engineers and researchers who are starting to investigate those extremely current and challenging subjects related to the software safety of autonomous road vehicles.

Please find below more detailed information about the paper.

N. Rajabli, F. Flammini, R. Nardone and V. Vittorini, “Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review,” in IEEE Access, vol. 9, pp. 4797-4819, 2021, doi: 10.1109/ACCESS.2020.3048047.

Abstract: Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.

Keywords: Vehicles; Autonomous automobiles; Safety; Software; Accidents; Roads; Systematics; Advanced driver assistance systems; automotive engineering; autonomous vehicles; cyber-physical systems; formal verification; intelligent vehicles; machine learning; system testing; system validation; vehicle safety



RAILS Project: first deliverable and dissemination results

Wednesday, September 23rd, 2020
Dear Colleagues,
I would like to inform you that the RAILS project has produced its first deliverable that can be downloaded from:
As part of project dissemination activities, we have organized the 1st International Workshop on “Artificial Intelligence for RAILwayS” (AI4RAILS):

The Springer proceedings of AI4RAILS are available at:

and can be downloaded for a limited time (until October 1st 2020) from:


Finally, the paper entitled “Low-Power Wide-Area Networks in Intelligent Transportation: Review and Opportunities for Smart-Railways”, co-authored with Linnaeus University master student Ruth Dirnfeld, has been recently presented at the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) according to the following updated program:

Presentation slides are available on slideshare.


Any feedback on all those initiatives and preliminary results is very much appreciated!


Prof. Francesco Flammini

RAILS Technical Manager, AI4RAILS Co-Chair



RAILS Project on ERCIM News

Tuesday, April 14th, 2020

An overview article about the H2020 RAILS Research Project has been recently published on ERCIM News:

ERCIM News 121 – RAILS

Project RAILS (Roadmaps for AI integration in the raiL Sector) selected for EU funding (Horizon2020 – Shift2Rail JU)

Tuesday, November 12th, 2019

The research project named RAILS (Roadmaps for AI integration in the raiL Sector) has been selected for EU funding by the Horizon 2020 Shift2Rail Joint Undertaking (call ID: S2R-OC-IPX-01-2019). The project will be in cooperation between Linnaeus University, Italian Interuniversity Consortium for Informatics (CINI), Delft University of Technology, University of Leeds, and will leverage on the input from big industry players like Hitachi Rail STS.



The overall objective of the RAILS research project is to investigate the potential of Artificial Intelligence (A.I.) approaches in the rail sector and contribute to the definition of roadmaps for future research in next generation signalling systems, operational intelligence, and network management. RAILS will address the training of PhD students to support the research
capacity in A.I. within the rail sector across Europe by involving research institutions in four different countries with a combined background in both computer science and transportation systems. RAILS will produce knowledge, ground breaking research and experimental proof-of-concepts for the adoption of A.I. in rail automation, predictive maintenance and defect detection, traffic planning and capacity optimization. To that aim, RAILS will combine A.I. paradigms with the Internet of Things, in order to leverage on the big amount of data generated by smart sensors and applications. The research activities will be conducted in continuity with ongoing research in railways, but the methodological and technological concepts developed in RAILS are expected to stimulate further innovation providing new research directions to improve reliability, maintainability, safety, security, and performance. With respect to safety, emerging threats and certification issues will be addressed when adopting A.I. in autonomous and cooperative driving, based on the concepts of “explainable A.I.” and “Trustworthy AI”. With respect to cyber-physical threat detection, innovative approaches will be developed based on A.I. models like Artificial Neural Networks and Bayesian Networks together with multi-sensor data fusion and artificial vision. Resilience and optimization techniques based on genetic algorithms and self-healing will be addressed to face failures and service disruptions as well as to increase efficiency and line capacity. All those techniques will pave the way to the development of the new “Railway 4.0”.


LNU Project Page


RAILS Project Website