ATRIUM analysis with AD-EYE and RCV-E 

This repository contains code for the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems, located at KTH Campus Valhallavägen, designed and developed by Naveen Mohan and others.

This particular respository serves as a store for the work performed during the first application made by AD-EYE to become an authorised test site for AD testing in Stockholm, Sweden. This application used AD-EYE code integrated with the Research Concept Vehicle from the ITRL lab at KTH.

This repository contains work from the

• ITRL team in the form of the RCV-E Capella library
• AD-EYE team in modelling the ADI in Capella
• AD-EYE team in performing the analysis of the integrated system using the ATRIUM process.

Note: The folder "Capella" contains the base software capella MBSE and requires java jre-8u221.

Once launched, by chosing the workspace "workspace", the user will be able to see and modify the model of ADI and its related library : RCVE.

The Atrium VP v1.0.0 has been added in the dropins folder of the base capella software. Although it can be used in both branches, the branch ADI_and_RCVE does not contain any Atrium elements. The Capella model in the branch Atrium_process contains the start an Atrium iteration, and can be continued by opening the Atrium UI on the diagram "Atrium process diagram" of ADI.

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

OPEN-KTH-3dMODELS: An open dataset of building models at KTH Campus Valhallavägen

Open-KTH-3dModels is a subproject of the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems.The dataset comprises of a series .blend files that have prominent buildings from KTH campus Valhallavägen. The dataset also contains PreScan compatible models that can be used wtih AD-EYE (https://www.adeye.se/)Visualisation video: https://www.youtube.com/watch?v=F6NfCiul3oELearn more at https://www.adeye.se/open-kth-3dmodels or contact adeye@md.kth.se

The AD-EYE testbed is based on the design presented in the work "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts"

Original paper: https://doi.org/10.4271/2019-01-0126

Preprint available at: https://arxiv.org/abs/1912.00448

Citation:

Naveen Mohan, Martin Törngren, "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts", SAE Technical Paper 19AE-0203/2019-01-0126, https://doi.org/10.4271/2019-01-0126

Notes:

Modelling work primarily performed by Lester Jose, during his internship with AD-EYE.

This repository contains code for the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems, located at KTH Campus Valhallavägen, designed and developed by Naveen Mohan and others.

This particular repository contains code for the tool support in following the process ATRIUM, a creation method for the Functional Safety Viewpoint, part of the SMART Architecture Framework designed by Naveen Mohan and others at KTH Mechatronics.

The tool is modelled as a viewpoint for Capella, using Capella Studio. The tool is implemented in Java.

This repository contains code for the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems, located at KTH Campus Valhallavägen, designed and developed by Naveen Mohan and others.

The specific contents of this repository enable the extraction of road and lane network definitions from a PreScan compatible .pex format, and output it into CSV files that comply with the format used by Autoware's Vector maps.The code in this repository was first created as part of a Embedded Systems capstone course at KTH in 2018.

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper. 

OPEN-KTH: An open Lidar dataset of KTH campus Vahlallavägen

Open-KTH is a subproject of the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems.

Learn more at https://www.adeye.se/open-kth or contact adeye@md.kth.se

The AD-EYE simulation platform is based on the description presented in the work:

Naveen Mohan, Martin Törngren, "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts", SAE Technical Paper 19AE-0203/2019-01-0126, https://doi.org/10.4271/2019-01-0126

Preprint available at: https://arxiv.org/abs/1912.00448

Data collected by students of the group "AD-EYE on Road" in the capstone course of the Mechatronics Masters program using the AD-EYE platform.

Mapping using the collected data was performed by Maxime Sainte Catherine from the AD-EYE team.

System and Method for Sensor failure resiliency during operation- Scania internal Application number 2021-0230

Automated Driving is revolutionizing many of the traditional ways of operation in the automotive industry. The impact on safety engineering of automotive functions is arguably one of the most important changes. There has been a need to re-think the impact of the partial or complete absence of the human driver (in terms of a supervisory entity) in not only newly developed functions but also in the qualification of the use of legacy functions in new contexts. The scope of the variety of scenarios that a vehicle may encounter even within a constrained Operational Design Domain, and the highly dynamic nature of Automated Driving, mean that new methods such as simulation can greatly aid the process of safety engineering. This paper discusses the need for early verification of the Functional Safety Concepts (FSCs), details the information typically available at this stage in the product lifecycle, and proposes a co-simulation platform named AD-EYE designed for exploiting the possibilities in an industrial context by evaluating design decisions and refining Functional Safety Requirements based on a reusable scenario database. Leveraging our prior experiences in developing FSCs for Automated Driving functions, and the preliminary implementation of co-simulation platform, we demonstrate the advantages and identify the limitations of using simulations for refinement and early FSC verification using examples of types of requirements that could benefit from our methodology.

Environment perception and representation are some of the most critical tasks in automated driving. To meet the stringent needs of safety standards such as ISO 26262 there is a need for efficient quantitative evaluation of the perceived information. However, to use typical methods of evaluation, such as comparing using annotated data, is not scalable due to the manual effort involved. There is thus a need to automate the process of data annotation. This paper focuses on the LiDAR sensor and aims to identify the limitations of the sensor and provides a methodology to generate annotated data of a measurable quality. The limitations with the sensor are analysed in a Systematic Literature Review on available academic texts and refined by unstructured interviews with experts. The main contributions are 1) the SLR with related interviews to identify LiDAR sensor limitations and 2) the associated methodology which allows us to generate world representations.