4.6 Article

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

Journal

SENSORS
Volume 21, Issue 22, Pages -

Publisher

MDPI
DOI: 10.3390/s21227583

Keywords

automotive perception sensors; sensor model; virtual testing; ADAS/AD function; automotive camera

Funding

  1. COMET K2 Competence Centers for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK)
  2. Austrian Federal Ministry for Digital and Economic Affairs (BMDW)
  3. Province of Styria (Dept. 12)
  4. Styrian Business Promotion Agency (SFG)

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This paper introduces a novel approach for modeling automotive perception sensors, which combines kernel density estimation with regression modeling to address position measurement errors. The method is suitable for automotive perception sensors that provide object-level position estimations, and has shown good performance in lateral and longitudinal position error after field data collection and experimental validation.
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.

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