A testing framework for predictive driving features with an electronic Horizon
This paper proposes a novel approach for automated functional testing of map-based fusion algorithms in complex vehicle networks. The hybrid data representation of detailed digital maps and physical automotive sensors provides an extended view of the ego-vehicle environment and thereby facilitates improved inferences and more competent decision-making. It has therefore been instrumental in the ongoing development of predictive driving features, e.g. fuel-efficient driving, traffic sign recognition and highway-pilot. The presented approach utilises a closed-loop Hardware-in-the-Loop (HiL) co-simulation framework to evaluate the performance of the decision level fusion algorithms. The method contains both the structural design and resource-efficient integration into the HiL test bench in the example of traffic sign recognition. In reality, discrepancy between visual and map data is omnipresent due to map errors, old map data or optical detection failure. Through fault injection, defined inconsistencies can be produced within the HiL simulation environment. Amongst others, the fault injection covers the placement and value of traffic signs. These failures can be used for robustness testing of the fusion algorithms. Subsequently, a method for correlation analysis between field observations and synthetic simulation is realised to extend the requirements-based test coverage adaptively and systematically by modular and parameterised scenario specifications. In summary, the results show that the extended HiL environment is capable of generating electronic Horizon data which can easily be adapted or extended.