Context Adaptive Fault Tolerant Multi-sensor fusion: Towards a Fail-Safe Multi Operational Objective Vehicle Localization

In many transport applications, one of the safety critical function is the localization. This is all the more true for land transport applications such as autonomous vehicles. While the democratization of satellite positioning systems, such as GPS, Galileo, Beidou or Glonass, has made it possible to consider a global solution applicable anywhere in the world, the principle of positioning by receiving signals from satellites more than twenty thousand kilometers away shows limits when they are confronted with disturbances related to the environment close to the receiver. However, for these safety-critical applications, the requirements are strong and sometimes even conflicting. The developed function must meet a defined level of precision, availability, continuity of service, integrity, operational safety and finally robustness to environment changes. Taken separately, these requirements can be achieved by actions recommended by the literature. For more precision and availability, coupling between absolute GNSS data and relative INS and odometer data, is recommended. To increase safety and integrity, a fault detection layer is essential, but this will negatively impact availability. One therefore needs a fault management layer. A harmonious policy, thought at the function design, makes it possible to achieve all the objectives. In this study, we propose a framework based on a tripartite approach: the tight fusion of GNSS and IMU data, the development of a diagnostic layer based on information theory and using the very promising alpha Rényi divergence, as well as a fault isolation layer. The diagnostic layer is designed to be robust and adaptive to changing environment through a deep neural network. The proposed framework is tested on data acquired in the field. Encouraging results allow to consider the generalization of the concept.