Abstract
This study presents the process employed in prototyping and early evaluation of automotive perception algorithms. The data generation was performed using an automotive virtual validation tool. The off-the-shelf simulation framework used was expanded to include phenomenological sensors model that allowed for a simplified simulation of radars, lidars, and cameras. This paper extends the description of the methods for the generation of control algorithms. The work presented also includes a description of relevant data fusion methods for building occupancy grids. Results were obtained by performing a comparison of algorithm results against ground-truth. This virtual validation was used to enable early definition and verification of system-level requirements, narrow down performance assessment methods, and identify performance limitations before data from real sensors are available.
Highlights
This article deepens the application of virtual validation methods used for occupancy grid fusion computation that are presented in the previous article [1]
The methodology presented in this article is based on the occupancy grid principles described by Elfes and Moravec as a tessellation of space into cells, where each cell contains a probabilistic estimate of its occupancy [2]
As the analysis focuses on the static grid, footprints from dynamic objects are removed
Summary
This article deepens the application of virtual validation methods used for occupancy grid fusion computation that are presented in the previous article [1]. The primary goal behind the use of virtual validation in the early stages of the project life cycle is the quick identification of system limitations. Identification of systems limitations at the concept phase is crucial for reduction in time and cost of the development. Virtual methods allow us to avoid severe concept reworks at later stages of the system design. The methodology presented in this article is based on the occupancy grid principles described by Elfes and Moravec as a tessellation of space into cells, where each cell contains a probabilistic estimate of its occupancy [2]. General Formula (1) is used to describe a two-dimensional grid of size NL number of cells in length by NW number of cells in width
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