Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps
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Autonomously driving vehicles require a complete and robust perception of the local environment. A main challenge is to perceive any other road users, where multi-object tracking or occupancy grid maps are commonly used. The presented approach combines both methods to compensate false positives and receive a complementary environment perception. Therefore, an environment perception framework is introduced that defines a common representation, extracts objects from a dynamic occupancy grid map and fuses them with tracks of a Labeled Multi-Bernoulli filter. Finally, a confidence value is developed, that validates object estimates using different constraints regarding physical possibilities, method specific characteristics and contextual information from a digital map. Experimental results with real world data highlight the robustness and significance of the presented fusing approach, utilizing the confidence value in rural and urban scenarios.
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