Periodic Reporting for period 2 - RAILGAP (RAILway Ground truth and digital mAP)
Reporting period: 2022-07-01 to 2024-09-30
Novel signalling systems are expected to use Digital Maps to implement ATO functions or to augment technologies such as EGNSS, inertial sensors or optical sensors for localization purposes. However, the construction of a Digital Map with current technologies requires expensive railway surveying campaigns. Additionally, the cost for their maintenance and update is a major barrier to their widespread adoption. With regard to the design of Digital Maps and trackside signalling subsystems, they are based on the knowledge of two types of data: (a) the longitudinal positions of Points Of Interests with respect to the track centreline and (b) the grades or slopes of the track segments adjacent to them. Therefore, as a first step, such designs require the acquisition or the verification of the data related to POIs (i.e. nominal values of locations and gradients along with their related accuracies) and this causes extra costs and time-consuming surveying activities leading to delays in the commissioning of the lines.
With regard to the evolution of CCS systems, a new verification infrastructure is also needed to characterize new on-board solutions in terms of train position, speed and acceleration errors, as well as to assess their expected performance. The performance analysis of signalling systems is in most of previous work analysed with project dependent mechanisms and procedures. Unfortunately, there is no agreed methodology for building “reference ground truth data” in railway applications. Up to now, the investigated Ground Truth methodology has required the use of the track database and the availability of absolute and relative reference measurement systems associated with the lines for which the Ground Truth had to be built. Moreover, these methodologies also suffer from other two limitations, i.e. they provide (a) “reference ground truth data” for the train position only and (b) no information about the accuracy of the provided “reference ground truth data”.
RAILGAP developed a methodology and related toolset for building and maintaining Digital Maps based on the post-processing of data recorded by trains equipped with COTS sensors like GNSS receivers, cameras, IMUs, and LIDARs. On the other side, a similar approach and development has been used to provide high accuracy reference data for carrying out error and performance analysis. The RAILGAP project reached three main objectives. The first one is the development of an innovative Ground Truth methodology and related toolset that overcome the above described constraints and limitations. The second objective is to perform field surveys by using measured physical entities collected with trains during commercial services for building and maintaining Digital Maps. The third one is the use of AI techniques to off-line process big amount of collected data to allow the extraction of the information required for building and maintaining Digital Maps.
With regard to the evolution of CCS systems, a new verification infrastructure is also needed to characterize new on-board solutions in terms of train position, speed and acceleration errors, as well as to assess their expected performance. The performance analysis of signalling systems is in most of previous work analysed with project dependent mechanisms and procedures. Unfortunately, there is no agreed methodology for building “reference ground truth data” in railway applications. Up to now, the investigated Ground Truth methodology has required the use of the track database and the availability of absolute and relative reference measurement systems associated with the lines for which the Ground Truth had to be built. Moreover, these methodologies also suffer from other two limitations, i.e. they provide (a) “reference ground truth data” for the train position only and (b) no information about the accuracy of the provided “reference ground truth data”.
RAILGAP developed a methodology and related toolset for building and maintaining Digital Maps based on the post-processing of data recorded by trains equipped with COTS sensors like GNSS receivers, cameras, IMUs, and LIDARs. On the other side, a similar approach and development has been used to provide high accuracy reference data for carrying out error and performance analysis. The RAILGAP project reached three main objectives. The first one is the development of an innovative Ground Truth methodology and related toolset that overcome the above described constraints and limitations. The second objective is to perform field surveys by using measured physical entities collected with trains during commercial services for building and maintaining Digital Maps. The third one is the use of AI techniques to off-line process big amount of collected data to allow the extraction of the information required for building and maintaining Digital Maps.
RAILGAP has made significant progress in its second reporting period: a) a rigorous characterization of LIDAR, IMU, and GNSS technologies tailored for the railway environment has been completed, enabling a better understanding of their suitability for railway applications; b) a sustainable, cost-effective, and infrastructure-independent solution has been developed for: i) generating high-accuracy Ground Truth; ii) building/updating Trackside Digital Map. RAILGAP aims to contribute to the roadmap for introducing EGNSS and satellite positioning in ERTMS. By addressing gaps identified in past initiatives, the project provides new methodologies for generating Ground Truth and Digital Maps without the need for infrastructure modifications or dedicated survey campaigns.
The consortium hosted an exploitation meeting in Malaga on June 20, 2024, where the innovative approach was demonstrated and discussed with key stakeholders from the railway (Europe’s Rail, EUG, other railway companies) and space sectors (EUSPA, ESA). This event facilitated the exploration of integration strategies for GNSS technology in railway systems and the discussion of future implementation steps.
Several scientific papers were published at prestigious conferences, including WCRR, TRA, ENC, and ION. RAILGAP results were also presented at the Panel Discussion on satellite positioning for Railways at INNOTRANS 2024, organized by UNIFE.
A dedicated project website (https://railgap.eu/(opens in new window)) has been established, featuring project information, results, and public deliverables. A video presentation was created and shown during the 2022 Shift2Rail Innovation Days. Additionally, two newsletters were produced, with around 100 printed copies distributed at events such as TRA 2024 and INNOTRANS. The digital versions are available on the website. RAILGAP also created a LinkedIn project account, where relevant updates and achievements were posted to maximize the project’s impact and raise awareness. A Final Conference was organized in Rome on October 17, 2024, gathering representatives from EUSPA and major stakeholders from the rail and space sectors.
The consortium hosted an exploitation meeting in Malaga on June 20, 2024, where the innovative approach was demonstrated and discussed with key stakeholders from the railway (Europe’s Rail, EUG, other railway companies) and space sectors (EUSPA, ESA). This event facilitated the exploration of integration strategies for GNSS technology in railway systems and the discussion of future implementation steps.
Several scientific papers were published at prestigious conferences, including WCRR, TRA, ENC, and ION. RAILGAP results were also presented at the Panel Discussion on satellite positioning for Railways at INNOTRANS 2024, organized by UNIFE.
A dedicated project website (https://railgap.eu/(opens in new window)) has been established, featuring project information, results, and public deliverables. A video presentation was created and shown during the 2022 Shift2Rail Innovation Days. Additionally, two newsletters were produced, with around 100 printed copies distributed at events such as TRA 2024 and INNOTRANS. The digital versions are available on the website. RAILGAP also created a LinkedIn project account, where relevant updates and achievements were posted to maximize the project’s impact and raise awareness. A Final Conference was organized in Rome on October 17, 2024, gathering representatives from EUSPA and major stakeholders from the rail and space sectors.
Traditional methods for generating Ground Truth in the railway field have several limitations. Often, these rely on pre-existing databases, which can be costly to create and require regular updates to maintain their accuracy. Past projects have involved installing dedicated sensors along the tracks (such as RFID tags or balises), but this approach entails high costs, long implementation times, and complex authorization processes. Additionally, these methods typically provide only positional data without information on accuracy, limiting reliability. RAILGAP overcomes these limitations by using COTS sensors, and developing an infrastructure-independent system that does not require ground equipment installation or modifications to existing signaling systems. This innovative approach generates highly accurate and reliable reference data not only for the position but also for the train’s speed, acceleration, and attitude. Similarly, traditional methods for creating and updating Digital Maps are costly and time-consuming, hindering their adoption even though they are considered a fundamental requirement for future signaling systems. Traditional methods require time-consuming and expensive survey campaigns. In addition, rail infrastructure is subject to constant change over time and additional costs are required to ensure that the digital map is regularly updated. To overcome these limitations, RAILGAP offers a more efficient solution by leveraging Artificial Intelligence and data from trains equipped with COTS sensors. The proposed approach can meet the needs of railway companies by allowing large amounts of data to be obtained at a lower cost than traditional methods. RAILGAP represents a significant step forward in the evolution of localization and digital mapping systems and can contribute to the adoption of GNSS-based solutions in the railway sector.