| Autor: |
Gies, Fabian, Danzer, Andreas, Dietmayer, Klaus |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 3859-3865 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ITSC.2018.8569235 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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