Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model- and data-based approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as super-resolution capabilities of several architectures.

With the recent advancements in radar systems, radar sensors offer a promising and effective perception of the surrounding. This includes target detection, classification and tracking. Compared to the state-of-the-art, where the state vector of classical tracker considers only localization parameters, this paper proposes an integrated Bayesian framework by augmenting state vector with feature embedding as appearance parameter together with localization parameter. In context of automotive vulnerable road users (VRUs) such as pedestrian and cyclist, the classical tracker poses multiple challenges to preserve the identity of the tracked target during partial or complete occlusion, due to low inter-class (pedestrian-cyclist) variations and strong similarity between intra-class (pedestrian-pedestrian). Subsequently, feature embedding corresponding to target’s micro-Doppler signature are learned using novel Bayesian based deep metric learning approaches. The tracker’s performance is optimized due to a better separability of the targets. At the same time, the classifiers’ performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of the classifier’s embedding vector. In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar.

Vehicular communication systems get more and more attention with the upcoming fifth and sixth generation. Hereby, the focus lies on the development of the co-design or co-existence of communications and sensing. So called joints communications and radar sensing systems are seen as one key technology of 6G. As joint systems will have shared waveforms and hardware platforms, there is a huge benefit in cost and space which is one essential argument for the automotive industry. However, to design such a system for new applications like platooning or intersection assistance a physical layer has to be set up to represent the real physical layer properly. The proposed system design closes the lack of such a simulation tool and allows for full physical layer simulations, including e.g. the hardware non-idealities and the channel model for 77GHz. The whole signal processing chain of the physical layer is built up and will be integrated in the higher layer state-of-the-art simulation frameworks like Veins or Artery in the next step. The communications design is developed in Simulink, whereas the sensing part is discussed. The proposed communications architecture covers several transmission approaches (serial, parallel), a CDMA based spreading, a radio frequency representation, the channel (simulated in a 3D-Ray-Tracing-tool) and the receiver structure (e.g. the synchronisation or the channel estimation). Several design criteria are discussed, like the serial or parallel design architecture, the maximum ratio combining or the phase and frequency compensation method. The whole system architecture is freely available ( https://doi.org/10.5281/zenodo.6482565 ) and in consequence, the different signal blocks and parameters can be enabled or disabled for evaluations according to future design criteria requirements.

Automatic radar based classification of automotive targets, such as pedestrians and cyclist, poses several challenges due to low inter-class variations among different classes and large intra-class variations. Further, different targets required to track in typical automotive scenario can have completely varying dynamics which gets challenging for tracker using conventional state vectors. Compared to state-of-the-art using independent classification and tracking, in this paper, we propose an integrated tracker and classifier leading to a novel Bayesian framework. The tracker’s state vector in the proposed framework not only includes the localization parameters of the targets but is also augmented with the targets’s feature embedding vector. In consequence, the tracker’s performance is optimized due to a better separability of the targets. Furthermore, the classifier’s performance is enhanced due to Bayesian formulation utilizing the temporal smoothing of classifier’s embedding vector.