Occupancy Grid Fusion Prototyping Using Automotive Virtual Validation Environment

Markiewicz, Paweł; Kogut, Krzysztof; Różewicz, Maciej; Skruch, Paweł; Starosolski, Roman

doi:10.1145/3284516.3284540

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…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.…”

Section: Introductionmentioning

confidence: 89%

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Developing Occupancy Grid with Automotive Simulation Environment

Markiewicz

Porębski

2020

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 89%

“…The disadvantages of virtual methodologies however mainly suffer from limited physical realism of both tests and modeled perception equipment. The scope of this research focuses on exploring the capabilities of virtual validation methods, and does not seek to determine the absolute performance of these methods [1].…”

Section: Introductionmentioning

confidence: 99%

Developing Occupancy Grid with Automotive Simulation Environment

Markiewicz

Porębski

2020

Applied Sciences

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“…The occupancy grid algorithm implementation was developed based on [ 23 , 28 ]. Grid map size was selected as

m with a cell resolution of

m based on previous highway grid parameter reviews [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation of the Highway Radar Occupancy Grid

Porębski

Kogut²

2021

Sensors

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The quality of environmental perception is crucial for automated vehicle capabilities. In order to ensure the required accuracy, the occupancy grid mapping algorithm is often utilised to fuse data from multiple sensors. This paper focuses on the radar-based occupancy grid for highway applications and describes how to measure effectively the quality of the occupancy map. The evaluation was performed using the novel grid pole-like object analysis method. The proposed assessment is versatile and can be applied without detailed ground truth information. The evaluation was tested with a simulation and real vehicle experiments on the highway.

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“…A typical set of object parameters includes: position -either in 2D or in 3D space, motion parameters such as velocity, acceleration, heading, and turn rate, parameters that determine the shape and size of the objects and the orientation of the shape. For some applications, the determination of the allowable (so-called freespace) or not allowable (so-called occupancy space) driving space is also required [65]. The perception system is exposed to many types of uncertainties.…”

Section: B Automotive Perception Systemsmentioning

confidence: 99%

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk¹,

Komorkiewicz²,

Skruch

et al. 2022

IEEE Access

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T HE paper proposes a formal approach for describing and evaluating the datasets that are used in automotive applications for machine learning, testing, and validation purposes. Proper, that is, qualitative and quantitative characterization of the datasets can simplify the analysis, evaluation, and comparison of perception-based algorithms designed for highly automated vehicles. Such formalism is also needed to achieve compliance with the automotive industry safety standards that have been recently introduced. Characterization in the form of size or type of raw data, number of recognized and classified objects, and environmental parameters is not perfectly suitable for describing both the static and dynamic aspects of automotive datasets; therefore, another approach is required. In this paper, an efficient method based on an object tracking mechanism, grid representation of the sensor field of view, heatmap concept, and Wasserstein metric is proposed. The efficiency of the method is demonstrated by its ability to handle both the size, properties, and diversity of the dataset, including static and time-varying aspects. The presented description can also be used to compare different datasets and to define the amount of data to be collected.

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Occupancy Grid Fusion Prototyping Using Automotive Virtual Validation Environment

Cited by 9 publications

References 9 publications

Developing Occupancy Grid with Automotive Simulation Environment

Developing Occupancy Grid with Automotive Simulation Environment

Performance Evaluation of the Highway Radar Occupancy Grid

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

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