Abstract:For the development of fully-autonomously driving vehicles, advanced capabilities for sensor systems are required. With modulation-based radar target simulators, complex traffic scenarios can be simulated for automotive radars at low costs. Yet the simulation principle relies on the timings of the chirp-sequence frequency modulated continous waveform. Since small timing variations can be purposely introduced on the radar's waveform e.g. for interference mitigation techniques, the assumption of ideal timings co… Show more
“…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 đť‘ mod (𝑡) of the targets actually detected by a CFAR algorithm [12]. This is demonstrated in the left column in Fig.…”
Section: Virtually Augmented Scenariosmentioning
confidence: 99%
“…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. 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].…”
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.
“…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 đť‘ mod (𝑡) of the targets actually detected by a CFAR algorithm [12]. This is demonstrated in the left column in Fig.…”
Section: Virtually Augmented Scenariosmentioning
confidence: 99%
“…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. 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].…”
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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