Periodic Reporting for period 2 - TAURO (Technologies for Autonomous Rail Operation)
Berichtszeitraum: 2022-01-01 bis 2022-12-31
Throughout the history of transportation, very few inventions have had the same impact as rail transport. One of the oldest and most established means of transportation, railways still provide efficient transportation of freight and passengers, but they stand to benefit from cutting-edge technology. The EU-funded TAURO project of Shift2Rail will shape the future of European rail transport by developing the technologies required to make autonomous rail transport a reality. It will achieve this by working on state-of-the-art systems for environmental perception, remote operation, automatic monitoring and diagnostics, and facilitating the transition to these new autonomous control systems.
The high-level objective of TAURO is to identify, analyse and finally propose suitable founding technologies for the future European automated and autonomous rail transport, to be further developed, certified and deployed through the activities planned for the Shift2Rail’s successor Europe’s Rail.
To achieve this, TAURO consortium has broken down the work into several technical working areas, that each deals with separate system elements, while all of them contributing to the overall goals of the project. These areas of work and their main challenges are:
• Artificial environment perception for automation: AI is not deterministic and usual rail standards namely EN50126/8/9 cannot be used for certification as such. A rail specific approach is required.
• Remote driving and command: Assure safety and security while providing performance and interoperable solutions.
• Automatic status monitoring and diagnostic for autonomous trains: Minimise human intervention to make autonomous trains autonomously resilient.
• Technologies supporting migration to ATO over ETCS: Accelerate the deployment of an interoperable ATO, enhance the maintainability and reliability of the delivered specifications and validate the stability and performance increase.
The high-level objective of TAURO is to identify, analyse and finally propose suitable founding technologies for the future European automated and autonomous rail transport, to be further developed, certified and deployed through the activities planned for the Shift2Rail’s successor Europe’s Rail.
To achieve this, TAURO consortium has broken down the work into several technical working areas, that each deals with separate system elements, while all of them contributing to the overall goals of the project. These areas of work and their main challenges are:
• Artificial environment perception for automation: AI is not deterministic and usual rail standards namely EN50126/8/9 cannot be used for certification as such. A rail specific approach is required.
• Remote driving and command: Assure safety and security while providing performance and interoperable solutions.
• Automatic status monitoring and diagnostic for autonomous trains: Minimise human intervention to make autonomous trains autonomously resilient.
• Technologies supporting migration to ATO over ETCS: Accelerate the deployment of an interoperable ATO, enhance the maintainability and reliability of the delivered specifications and validate the stability and performance increase.
During the second reporting period (2022) the overall goal of finalising the TAURO’s specifications for the new technologies was achieved. This included the architecture specification for the remote driving , the common database for AI training and interior perception. This also meant the completeness of milestone MS4.
Another relevant activity about train positioning ) started and feasibility studies on the applicability of the SLAM (Simultaneous Location and Mapping) were carried out with good results. Tests conducted on a tram of the Spanish city of Zaragoza led to the recording of a video that was unveiled during InnoTrans 2022.
First results in automating monitoring and diagnostics functions are already available. The definition of use cases and scenarios for automated monitoring of onboard systems was released.
Also good progress is coming from GoA3/4 architecture and modelling, complementing the work in IP2 X2Rail-4.. Despite its slow start in 2021, once the synchronisation with the IP2 X2Rail-4 project was achieved and the exact scope for the modelling was identified, is running now at full speed and results will be ready to be integrated with the overall GoA3/4 model by the end of TAURO project. The selected part that is being modelled refers to the digital map and its interfaces.
Another relevant activity about train positioning ) started and feasibility studies on the applicability of the SLAM (Simultaneous Location and Mapping) were carried out with good results. Tests conducted on a tram of the Spanish city of Zaragoza led to the recording of a video that was unveiled during InnoTrans 2022.
First results in automating monitoring and diagnostics functions are already available. The definition of use cases and scenarios for automated monitoring of onboard systems was released.
Also good progress is coming from GoA3/4 architecture and modelling, complementing the work in IP2 X2Rail-4.. Despite its slow start in 2021, once the synchronisation with the IP2 X2Rail-4 project was achieved and the exact scope for the modelling was identified, is running now at full speed and results will be ready to be integrated with the overall GoA3/4 model by the end of TAURO project. The selected part that is being modelled refers to the digital map and its interfaces.
Updated state-of-the-art:
During the execution of the European H2020 programme (2014-2022), many projects brought together stakeholders of the automotive industry for relevant safety and validation thematic areas. Many of them included particular developments or focus actions, but in general, they all contributed to the establishment of baseline know-how and de-facto standards in testing and validations methods for autonomous driving. To name a few, advances in data sharing and AI development were proposed and explored in ADAS&ME, AutoMate, AUTOPILOT, AVENUE, SHOW, BRAVE, CARAMEL, CoEXist, Dream4Cars, ENABLE-S3, ENSEMBLE, FABULOS, HEADSTART, ICT4CART, INFRAMIX, InLane, L3Pilot, MAVEn, Interact, SLAIN, TransAID, VI-DAS, 5G-Croco or 5G-MOBIX.
In parallel, at European level, other public and private initiatives have started to consolidate the research outcomes of these projects, including the EU expert group on Connected, Cooperative and Automated Mobility (CCAM), which, among other topics, aims to provide a Common Evaluation Methodology (CEM) and Data Sharing Framework (DSF) for Connected Automated Driving (CAD). Recently finished and ongoing projects are now pivoting around these concepts, such as the ARCADE Coordination and Support Action, which developed an online Knowledge Base (KB) gathering up to date information of CCAM related R&I projects, testing and piloting activities in Europe, regulation and policies, standards and impact assessment and data sharing methodologies. Currently, the FAME project aims to take over the results of ARCADE, and give continuity to the published KB. Focusing on scenario-based testing approaches, the Horizon Europe SUNRISE project aims to provide the first European-level Federated Scenario database for CAD testing.
Considering scientific initiatives, the most relevant contributions on AI development and testing are about the publication or massive multi-sensor datasets that can be used to train and validate models for environment perception (cameras, LIDAR, RADAR), and general scene understanding (e.g. maneuver analysis). In the last few years (2020-2022), it is noteworthy that large private companies are releasing professional-level datasets open for research purposes, such as the 3DHD CityScenes (Volkswagen), NuPlan (extension of NuScenes), MIT-AVT, Waymo Motion, Uber Pit30M, Ford Autonomous Vehicle Dataset, Drive&Act, DMD (Driver Monitoring Dataset), etc.
In the railway sector, two main datasets were recently released: FRSign, a dataset for traffic light detection and recognition, was published in 2020 as part of the TAS (Safe Autonomous Land Transport) project; Railsem19, a dataset for semantic rail scene segmentation, was published in 2019 on the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Since AI is the building block of most advances in CAD, there are also important trends on AI certification under the scope of Europe's AI strategy. Since its publication in 2018, several aspects of AI are being analyzed and recommendations are produced, such as the EU Open Data Directive, European Data Governance Act, EU AI Act, etc. Bodies such as the European AI Alliance have produced Ethics guidelines for trustworthy AI (2019), with operational tool assessment list for trustworthy AI (ALTAI, 2020), and general definition of best practices for AI practitioners.
During the execution of the European H2020 programme (2014-2022), many projects brought together stakeholders of the automotive industry for relevant safety and validation thematic areas. Many of them included particular developments or focus actions, but in general, they all contributed to the establishment of baseline know-how and de-facto standards in testing and validations methods for autonomous driving. To name a few, advances in data sharing and AI development were proposed and explored in ADAS&ME, AutoMate, AUTOPILOT, AVENUE, SHOW, BRAVE, CARAMEL, CoEXist, Dream4Cars, ENABLE-S3, ENSEMBLE, FABULOS, HEADSTART, ICT4CART, INFRAMIX, InLane, L3Pilot, MAVEn, Interact, SLAIN, TransAID, VI-DAS, 5G-Croco or 5G-MOBIX.
In parallel, at European level, other public and private initiatives have started to consolidate the research outcomes of these projects, including the EU expert group on Connected, Cooperative and Automated Mobility (CCAM), which, among other topics, aims to provide a Common Evaluation Methodology (CEM) and Data Sharing Framework (DSF) for Connected Automated Driving (CAD). Recently finished and ongoing projects are now pivoting around these concepts, such as the ARCADE Coordination and Support Action, which developed an online Knowledge Base (KB) gathering up to date information of CCAM related R&I projects, testing and piloting activities in Europe, regulation and policies, standards and impact assessment and data sharing methodologies. Currently, the FAME project aims to take over the results of ARCADE, and give continuity to the published KB. Focusing on scenario-based testing approaches, the Horizon Europe SUNRISE project aims to provide the first European-level Federated Scenario database for CAD testing.
Considering scientific initiatives, the most relevant contributions on AI development and testing are about the publication or massive multi-sensor datasets that can be used to train and validate models for environment perception (cameras, LIDAR, RADAR), and general scene understanding (e.g. maneuver analysis). In the last few years (2020-2022), it is noteworthy that large private companies are releasing professional-level datasets open for research purposes, such as the 3DHD CityScenes (Volkswagen), NuPlan (extension of NuScenes), MIT-AVT, Waymo Motion, Uber Pit30M, Ford Autonomous Vehicle Dataset, Drive&Act, DMD (Driver Monitoring Dataset), etc.
In the railway sector, two main datasets were recently released: FRSign, a dataset for traffic light detection and recognition, was published in 2020 as part of the TAS (Safe Autonomous Land Transport) project; Railsem19, a dataset for semantic rail scene segmentation, was published in 2019 on the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Since AI is the building block of most advances in CAD, there are also important trends on AI certification under the scope of Europe's AI strategy. Since its publication in 2018, several aspects of AI are being analyzed and recommendations are produced, such as the EU Open Data Directive, European Data Governance Act, EU AI Act, etc. Bodies such as the European AI Alliance have produced Ethics guidelines for trustworthy AI (2019), with operational tool assessment list for trustworthy AI (ALTAI, 2020), and general definition of best practices for AI practitioners.