Development and Experimental Validation of an Intelligent Camera Model for Automated Driving

Simon Genser*, Stefan Muckenhuber, Selim Solmaz, Jakob Reckenzaun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The virtual testing and validation of advanced driver assistance system and automated driving (ADAS/AD) functions require efficient and realistic perception sensor models. In particular, the limitations and measurement errors of real perception sensors need to be simulated realistically in order to generate useful sensor data for the ADAS/AD function under test. In this paper, a novel sensor modeling approach for automotive perception sensors is introduced. The novel approach combines kernel density estimation with regression modeling and puts the main focus on the position measurement errors. The modeling approach is designed for any automotive perception sensor that provides position estimations at the object level. To demonstrate and evaluate the new approach, a common state-of-the-art automotive camera (Mobileye 630) was considered. Both sensor measurements (Mobileye position estimations) and ground-truth data (DGPS positions of all attending vehicles) were collected during a large measurement campaign on a Hungarian highway to support the development and experimental validation of the new approach. The quality of the model was tested and compared to reference measurements, leading to a pointwise position error of
9.60% in the lateral and 1.57% in the longitudinal direction. Additionally, the modeling of the natural scattering of the sensor model output was satisfying. In particular, the deviations of the position measurements were well modeled with this approach.
Original languageEnglish
Article number7583
Number of pages22
JournalSensors
Volume21
Issue number22
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • automotive perception sensors; sensor model; virtual testing; ADAS/AD function; automotive camera
  • ADAS/AD function
  • Virtual testing
  • Sensor model
  • Automotive camera
  • Automotive perception sensors

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Electrical and Electronic Engineering
  • Automotive Engineering
  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Biochemistry

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Application

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