Environment Adaptive Diagnostic Framework For Safe Localization of Autonomous Vehicles

For an autonomous terrestrial transportation system, the ability to determine its position is essential in order to allow other functions, such as control or perception, to be carried out without danger. Thus, the criticality of these functions generates strong requirements in terms of safety/integrity, availability and accuracy. In the present paper, a multilevel positioning framework is proposed to adapt the navigation system to a wide range of environmental contexts. In order to improve the availability and accuracy, a tight coupling method of Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and vehicle’s odometry measurements based on nonlinear information filter (NIF) is used. Then, an adaptive diagnostic layer is investigated to adjust the trade-off between safety and other operational requirements. Its principal role is to deal with sensors errors. The use of parametric residuals, coupled with a deep neural network (DNN), makes it possible to select at each instant, the appropriate residual allowing, in the environment crossed, to maximize the detectability of measurement faults. This paper focuses on the conceptual approach and the implementation of this framework in order to adapt to the operating context (open sky, sub-urban, urban, covered …). Finally, to validate the performance of the proposed approach, tests are done with real trajectory showing encouraging position estimation results.