Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems

Autor:	Reichert, Hannes, Lang, Lukas, Rösch, Kevin, Bogdoll, Daniel, Doll, Konrad, Sick, Bernhard, Reuss, Hans-Christian, Stiller, Christoph, Zöllner, J. Marius
Rok vydání:	2021
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
DOI:	10.1109/ISC253183.2021.9562912
Popis:	Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair the perception models' transferability to new sensor setups, which continuously occur due to the market's competitive nature. We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD). For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups, and identify critical paths towards an abstraction of sensor data from multiple perception configurations. Comment: Hannes Reichert, Lukas Lang, Kevin R\"osch and Daniel Bogdoll contributed equally. Accepted for publication at ISC2 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.06896 Zobrazit plný text záznamu View this record from Arxiv