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Drone-based Generation of Sensor Reference and Training Data for Highly Automated Vehicles.

, , , , , and . ITSC, page 3067-3074. IEEE, (2021)

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Using Drones as Reference Sensors for Neural-Networks-Based Modeling of Automotive Perception Errors**The research leading to these results is funded by the Federal Ministry for Economic Affairs and Energy within the project "VVM - Verification and Validation Methods for Automated Vehicles Level 4 and 5". The authors would like to thank the consortium for the successful cooperation., , , , , and . IV, page 708-715. IEEE, (2020)Drone-based Generation of Sensor Reference and Training Data for Highly Automated Vehicles., , , , , and . ITSC, page 3067-3074. IEEE, (2021)The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany., , , , , and . IV, page 958-964. IEEE, (2022)Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections., , , and . VEHITS, page 346-351. SciTePress, (2017)The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems., , , and . ITSC, page 2118-2125. IEEE, (2018)6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment., , , , , , , , , and 4 other author(s). CoRR, (2020)Continuously Improving Model of Road User Movement Patterns using Recurrent Neural Networks at Intersections with Connected Sensors., , and . VEHITS, page 319-326. SciTePress, (2019)The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections., , , , , and . IV, page 1929-1934. IEEE, (2020)The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany., , , , and . ITSC, page 1-6. IEEE, (2020)