# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 5 Conclusions and Discussion  
In this paper, we extended a pillar detection network with our novel Sparsity-Robust Feature Fusion (SRFF) neck to detect smaller objects with just a few points from 4D radar data. Our novel Sparsity-Robust Feature Fusion (SRFF) combines high- and low-level features from multiple resolutions to enhance both localization and classification. The low-level features aid in better localization, whereas the high-level features allow for better classification. The effective receptive field of the backbone layers significantly impacts the amount of valid data in feature maps produced from sparse data. Through this multi-resolution approach, the network is more robust for classes with few points, as it combines features with different sparsities due to their effective receptive field. We studied lightweight attention mechanisms for an intelligent fusion of these features, improving the detection performance of vulnerable road users with negligible additional overhead. Our contributions are supported by extensive experiments on the well-known View-of-Delft dataset. At this point, we want to emphasize that our results are based on 4D radar sensors only. These have the advantage that the results are independent of weather conditions and also work reliably at night and in the rain. This cannot be proven quantitatively with the View-of-Delft dataset, but the physical properties of the sensors confirm this claim [5]. When comparing our results with detections from high-resolution images or quasi-dense LiDAR point clouds under good visibility conditions, our method exhibits worse accuracy. In bad weather conditions, however, various publications have shown that the accuracy of camera- and LiDAR-based methods drops significantly and sometimes approaches zero [38]. This property of 4D radar makes it suitable for standalone systems or in synergy with other sensors in order to compensate for their disadvantages. For maximum robustness, a combination of different sensor types (e.g., LiDAR or camera) is recommended to compensate for the disadvantages of the individual sensors. This is why we are convinced that we have made an important contribution to research in the automotive sector with a purely radar-based processing system. In the context of future work, our method could be explored in related modalities, such as LiDAR or depth sensing. Additionally, the lightweight nature of our method lends itself to subsequent research on real-time detection. Figure 4: Qualitative evaluation of our pillar-based network with the novel SRFF module on the View-of-Delft dataset [5]. The network is able to detect smaller objects with just a few points from 4D radar data. The classes are color-coded: _cars_ (blue), _pedestrians_ (green), and _cyclists_ (red). **Author Contributions:** Conceptualization, [PERSON] ([PERSON]), L.R. ([PERSON]), N.E. and O.W. ; methodology, L.R. ([PERSON]), L.R. ([PERSON]), N.E. and O.W. ; software, L.R. ([PERSON]) and L.R. ([PERSON]); validation, [PERSON] ([PERSON]) and L.R. ([PERSON]); formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]), [PERSON] ([PERSON]), N.E. and O.W. ; data curation, [PERSON] ([PERSON]) and L.R. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]), N.E. and O.W. ; writing--review and editing, [PERSON] ([PERSON]), L.R. ([PERSON]), N.E. and O.W. ; visualization, [PERSON] ([PERSON]), N.E. and O.W. ; supervision, N.E. and O.W. ; project administration, N.E. and O.W. ; funding acquisition, O.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partly funded by the Federal Ministry of Education and Research, Germany, under the project PreciRaSe (01 IS23023B). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** VoD data were obtained from [PERSON] et al.