Evaluation method and results for the accuracy of an automotive occupancy grid

Grewe, Ralph; Komar, Matthias; Hohm, Andree; Lueke, Stefan; Winner, Hermann

doi:10.1109/icves.2012.6294297

Cited by 13 publications

(10 citation statements)

References 19 publications

(32 reference statements)

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“…Due to the probabilistic cell representation, neither ghost targets are represented as real targets nor hidden targets as free space, which enables a highly accurate and very robust map. For many years, the radar sensors were not yet powerful enough to create high-resolution grid maps of the environment, but today's powerfull sensors enable a highresolution image of the environment [104]. A GNSS-based localization of the sensor is nowadays sufficient to create an occupancy grid map out of radar data [101], [105]- [107].…”

Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: Grid Mapping and Automotive Sarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…They are therefore usually dimensioned empirically to allow the viewer to best associate the OG with the camera image. The authors of [21] showed that significant quantization errors occur for edge lengths ƒ b^Ç 25 cm. In this paper, we use ƒ b^.…”

Section: ) Implementation In 3dmentioning

confidence: 99%

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

et al. 2020

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Radar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle. Moreover, numerous successes have already been achieved regarding the classification of such objects. However, the advantage of instantaneous velocity measurement is lost when detecting static objects, so that their classification is much more demanding. In this paper, we use semantic segmentation networks to distinguish between frequently occurring infrastructure objects. The resulting semantic grids provide a location-based classification of the vehicle environment. Since even modern radars have a significantly poorer angular resolution than lidars, the relatively thin radar point cloud is accumulated in advance and transformed into 2D or 3D grids that act as network inputs. Occupancy grids are particularly advantageous here, since they calculate not only the obstacles but also the free spaces. With suitable parameter selection, which is very challenging due to the complexity of radar measurement, the resulting grids allow for good association with camera images. Finally, in order to evaluate possible advantages of 3D grids as network input with respect to the segmentation result, we created and evaluated a simulation dataset and two different real-world datasets in car parks and on motorways. As a result, Jaccard coefficients between 81% and 88% were achieved, depending on the dataset. It was also found that 3D input images lead to improvements in the car park dataset.

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“…The occupied cells ratio (OCR) metric is chosen here, which is based on a cell-wise comparison of the real and simulated occupancy grid. The OCR describes the relation of true classified, occupied cells in the simulated occupancy grid G = {c 1 , ...,c J } to the total number of occupied cells in the real occupancy grid G = {c 1 , ..., c I } [25],…”

Section: A Metrics On Point Cloud Interface (If2)mentioning

confidence: 99%

Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

Rosenberger

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Hueh

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Simulation-based testing is seen as a major requirement for the safety validation of highly automated driving. One crucial part of such test architectures are models of environment perception sensors such as camera, lidar and radar sensors. Currently, an objective evaluation and the comparison of different modeling approaches for automotive lidar sensors are still a challenge. In this work, a real lidar sensor system used for object recognition is first functionally decomposed. The resulting sequence of processing blocks and interfaces is then mapped onto simulation methods. Subsequently, metrics applied to the aforementioned interfaces are derived, enabling a quantitative comparison between simulated and real sensor data at different steps of the processing pipeline. Benchmarks for several existing sensor models at a concrete selected interface are performed using those metrics by comparing them to measurements gained from the real sensor. Finally, we outline how metrics on low-level interfaces can correlate with results on more abstract ones. A major achievement of this work lies within the commonly accepted interfaces and a common understanding of real and virtual lidar sensor systems and, even more important, an initial guideline for the quantitative comparison of sensor models with the ambition to support future validation of virtual sensor models.

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Evaluation method and results for the accuracy of an automotive occupancy grid

Cited by 13 publications

References 19 publications

Automotive Radar — From First Efforts to Future Systems

Automotive Radar — From First Efforts to Future Systems

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

Benchmarking and Functional Decomposition of Automotive Lidar Sensor Models

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