Sensors for Automated Driving

Stefan Muckenhuber (), Kenan Softic (), Anton Fuchs (), Georg Stettinger () and Daniel Watzenig ()
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Stefan Muckenhuber: Scientist, University of Graz
Kenan Softic: VIRTUAL VEHICLE Research GmbH
Anton Fuchs: Virtual Vehicle Research Center
Georg Stettinger: VIRTUAL VEHICLE Research GmbH
Daniel Watzenig: VIRTUAL VEHICLE Research GmbH

A chapter in Autonomous Vehicles, 2021, pp 115-146 from Springer

Abstract: Abstract A sensor system capable of supporting automated driving functions needs to provide both reliable localization of the vehicle and robust environment perception of the vehicle’s surrounding. The following chapter introduces the working principles and the state of the art of automotive sensors for localization (GNSS and INS) and environment perception (camera, radar and LIDAR), correspondingLight Detection And Ranging (LIDAR) sensormodelsSensor modeland sensor fusionSensor fusion techniques. Sensor models will allow for the replacement of conventional test drives and physical component tests by using simulations in virtual test environments to meet the increasing requirements of automated vehicles with respect to development costs, time and safety. Considering the multitude and complexity of possible environmental conditions, realistic simulation of perception sensors is a particularly demanding topic. To increase the performance of a sensor system, compensate for limitations of each sensor modality, and increase the overall robustness of the system, sensor fusion techniques are an important subject in automotive research.

Keywords: Automated driving; Automotive sensors; Sensor models; Sensor fusion (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:perchp:978-981-15-9255-3_6

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DOI: 10.1007/978-981-15-9255-3_6

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