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Autonomous integrity monitoring of navigation maps on board intelligent vehicles

Abstract : Several Intelligent Vehicles capabilities from Advanced Driving Assistance Systems (ADAS) to Autonomous Driving functions depend on a priori information provided by navigation maps. Whilst these were intended for driver guidance as they store road network information, today they are even used in applications that control vehicle motion. In general, the vehicle position is projected onto the map to relate with links in the stored road network. However, maps might contain faults, leading to navigation and situation understanding errors. Therefore, the integrity of the map-matched estimates must be monitored to avoid failures that can lead to hazardous situations. The main focus of this research is the real-time autonomous evaluation of faults in navigation maps used in intelligent vehicles. Current passenger vehicles are equipped with proprioceptive sensors that allow estimating accurately the vehicle state over short periods of time rather than long trajectories. They include receiver for Global Navigation Satellite System (GNSS) and are also increasingly equipped with exteroceptive sensors like radar or smart camera systems. The challenge resides on evaluating the integrity of the navigation maps using vehicle on board sensors. Two types of map faults are considered: Structural Faults, addressing connectivity (e.g., intersections). Geometric Faults, addressing geographic location and road geometry (i.e. shape). Initially, a particular structural navigation map fault is addressed: the detection of roundabouts absent in the navigation map. This structural fault is problematic for ADAS and Autonomous Driving. The roundabouts are detected by classifying the shape of the vehicle trajectory. This is stored for use in ADAS and Autonomous Driving functions on future vehicle trips on the same area. Next, the geometry of the map is addressed. The main difficulties to do the autonomous integrity monitoring are the lack of reliable information and the low level of redundancy. This thesis introduces a mathematical framework based on the use of repeated vehicle trips to assess the integrity of map information. A sequential test is then developed to make it robust to noisy sensor data. The mathematical framework is demonstrated theoretically including the derivation of definitions and associated properties. Experiments using data acquired in real traffic conditions illustrate the performance of the proposed approaches.
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  • HAL Id : tel-01092628, version 2

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Clément Zinoune. Autonomous integrity monitoring of navigation maps on board intelligent vehicles. Other [cs.OH]. Université de Technologie de Compiègne, 2014. English. ⟨NNT : 2014COMP1972⟩. ⟨tel-01092628v2⟩

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