Safe and accurate localization solution for autonomous vehicles travelling in a constrained environment – road/rail – LOCSP

- First, on fault tolerant data fusion and cooperative solutions. It will consist in developing a framework for Fault Detection and Exclusion (FDE) for a Collaborative Localization (CL) of a multi-vehicle fleet, equipped with multi-sensors solutions.
- Second LOCSP will investigate the different techniques for interference detection and mitigation, evaluate and compare their respective performance. In particular, we will investigate the capacity of these different techniques to face RFI and in particular, the one from RAIM algorithms (Receiver Autonomous Integrity Monitoring) as well as the behavior of fusion algorithms in a PhD work. M3Systems will provide the tools for interference injection in the dataset.

In the project proposed, a reference database will be composed, as a representative set of crossed environments (rural, urban, forests) and feared events (multipath, reflected paths, interferences… These data will result from previous campaigns collected by the consortium members and will be complemented by specific other campaigns. Once public, this database shall allow everyone (scientific and industrial communities) to compare performance of positioning solutions on a common frame.

With two PhDs, the project will also propose new solutions for interference detection and mitigation and the development of a collaborative localisation solution able to detect and isolate sensor failures.

LOCSP objectives are then: (i) to compare performance reached by existing solutions; (ii) to propose a panel of solutions and algorithms with growing costs and complexity, in order to analyzed their behavior in the presence of faults in order to define their limits and strengths.

Adaptative Diagnosis for Fault Tolerant Data Fusion Based on alpha-Rényi Divergence Strategy for Vehicle Localization, M. Khoder, N. Ait Tmazirte, M. El Badaoui El Najjar, N. Moubayed. MDPI - Entropy, April 2021. DOI: 10.3390/e23040463

Navigation Context Adaptive Fault Detection and Exclusion Strategy based On Deep Learning & Information Theory: Application To a GNSS/IMU integration, Nesrine Harbaoui, Nourdine Ait Tmazirte, Khoder Makkawi, Maan El Badaoui El Najjar, ION GNSS+, Sept. 2021

Revolution of the transportation systems (evolving toward autonomous cars or trains in a world where digitalization will be everywhere) implies the development of a panel of embedded technologies among which localization and communication are key elements. In this framework, LOCSP (Solution de LOCalisation Sûre et Précise) will focus on the localization function and in particular on the requirement for accurate and safe positions. Indeed, the new positioning solutions shall provide on one hand the minimum accuracy required by the application (road level, track identification, position of the vehicle on a track…) and, on the other hand, ensure the user that the position can be trusted through the development of integrity monitoring solutions. Most of the solutions developed exploit the benefits of heterogeneous sensor fusion for a better robustness and accuracy.
LOCSP gathers two research teams (IFSTTAR and CRIStAL) and one SME (M3 Systems) in order to develop and compare safe and accurate localization solutions even in presence of faults and thus, answer the two following challenges : make the position more robust and accurate by implementing the most recent technologies and hybridizations techniques ; Allow everyone to benefit from a complete and detailed evaluation of the different solutions.
LOCSP intends, on the one hand, to complement existing studies for better performance, and, on the other hand, answer to need of comparison platforms of any algorithms developed in a different team.
Researches leaded in the framework of LOCSP will bring contributions on the two following topics in particular :
- First, on fault tolerant data fusion and cooperative solutions. It will consist in developing a framework for Fault Detection and Exclusion (FDE) for a Collaborative Localization (CL) of a multi-vehicle fleet, equipped with multi-sensors solutions.
- Second LOCSP will investigate the different techniques for interference detection and mitigation, evaluate and compare their respective performance. In particular, we will investigate the capacity of these different techniques to face RFI and in particular, the one from RAIM algorithms (Receiver Autonomous Integrity Monitoring) as well as the behavior of fusion algorithms in a PhD work. M3Systems will provide the tools for interference injection in the dataset.
In the project proposed, a reference database will be composed, as a representative set of crossed environments (rural, urban, forests) and feared events (multipath, reflected paths, interferences… These data will result from previous campaigns collected by the consortium members and will be complemented by specific other campaigns. Once public, this database shall allow everyone (scientific and industrial communities) to compare performance of positioning solutions on a common frame.
LOCSP objectives are then: (i) to compare performance reached by existing solutions; (ii) to propose a panel of solutions and algorithms with growing costs and complexity, in order to analyzed their behavior in the presence of faults in order to define their limits and strengths.