2018/11/01 by Fabian Gies, Andreas Danzer, Klaus Dietmayer · 1 citation
Computer Science · Environmental Science · #Digital mapping #False positive paradox #Grid #Impact of Light on Environment and Health #Object (grammar) #Occupancy #Occupancy grid mapping #Perception #Robustness (evolution) #Target Tracking and Data Fusion in Sensor Networks #Video Surveillance and Tracking Methods #cs.RO
paper · pdf · doi:10.1109/itsc.2018.8569235
published as 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2018, pp. 3859-3865
openalex publication_date 2018/11/01 · arxiv created 2018/12/20 · openalex created_date 2018/12/22 · arxiv updated 2020/03/26 · openalex updated_date 2026/08/05
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.