Variable Traffic Scenario Generation for Testing Automotive Radar Sensors

Rippl, Patrick; Schoeder, Pirmin; Grebner, Timo; Waldschmidt, Christian; Walter, Thomas

doi:10.23919/eurad54643.2022.9924859

Cited by 1 publication

(3 citation statements)

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“…With the presented RTS, it is possible to let an RuT record arbitrary combinations of scenarios and targets [13], as long as lists of detectable targets in the observed scene are available. One possible way to create a scenario is to replay real measurements by composing 𝑠 mod (𝑡) of the targets actually detected by a CFAR algorithm [12].…”

Section: Virtually Augmented Scenariosmentioning

confidence: 99%

“…Moreover, a hardware-based radar target simulator (RTS) is chosen for the generation of the augmented radar data for a more realistic simulation. Radar target simulation is an emerging field that aims to close the gap between software simulations and physical test drives [10]- [13]. While software simulations fully rely on sensor models, a radar target simulation employs actual radar sensors, thus eliminating the need to model the radar sensor.…”

Section: Introductionmentioning

confidence: 99%

“…The basis of the presented approach is the replay of real measurements from target lists [12]. Before replay, the target lists are augmented by reflections from virtual objects [13]. Finally, a radar records the virtually augmented scene fed to the RTS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Virtually Augmented Radar Measurements With Hardware Radar Target Simulators for Machine Learning Applications

Kern,

Schoeder,

Waldschmidt

2024

IEEE Sens. Lett.

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The acquisition of machine learning (ML) datasets by measurements for automotive radar data requires many resources and time. On the other hand, the simulation of complex traffic environments with sufficient level-of-detail is challenging, too. In this paper, a middle way is proposed in which real measurements are virtually augmented by reflections obtained from simulation models. The augmented measurements are then replayed with a hardware-based radar target simulator (RTS). This enables the fast creation of application-specific datasets as well as advanced functionality tests of algorithms at deployment. To demonstrate the efficacy of the virtually augmented radar data, a set of test drives is augmented by virtual pedestrians performing traffic gestures. Then, a classifier trained on the created dataset is demonstrated to achieve a high classification accuracy of 84.0 % on real test data.

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Section: Virtually Augmented Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%