# Radar Voxel Fusion for 3D Object Detection
## 5 Discussion
When analyzing the errors in our predictions, we see that the regressed parameters of the predicted bounding boxes are not as precise as the ones of state-of-the-art networks. The validation loss curves for our network are shown in Figure 5. The classification loss overfits before the regression loss converges. Further studies need to be performed in order to further balance the losses. One approach could be to first only train the regression and direction loss. The classification loss is then trained in a second stage. Additionally, further experiments will be performed to fine tune the anchor matching thresholds to the data set to get a better detection result. The tuning of this outer optimization loop requires access to extensive GPU power to find optimal hyperparameters. For future work, we expect the hyperparameters to influence the absolute detection accuracy greatly as simple strategies such as data augmentation could already improve the overall performance. The focus of this work lies in the evaluation of different data fusion inputs relative to a potent baseline network. For this evaluation, we showed a vast amount of evidence to motivate our fusion scheme and network parameterization. The simulated depth camera did not provide a better detection result than the lidar-only detection. This and the late fusion approach show that a simple fusion assumption in the manner of \"more sensor data, better detection result\" does not hold true. The complexity introduced by the additional data decreased the overall detection result. The decision for an early fusion system is therefore dependent on the sensors and the data quality available in the sensors. For all investigated sub data sets, we found that early fusion of radar and lidar data is beneficial for the overall detection result. Interestingly, the usage of 10 lidar sweeps increased the detection performance of the fusion network over the proposed baseline. This result occurred despite the fact that the accumulated lidar data leads to blurry contours for moving objects in the input data. This is especially disadvantageous for objects moving at a high absolute speed. For practical applications, we therefore use only three sweeps in our network, as the positions of moving objects are of special interest for autonomous driving. The established metrics for object detection do not account for the importance of surrounding objects. We assume that the network trained with 10 sweeps performs worse in practice, despite its higher AP score. Further research needs to be performed to establish a detection metric tailored for autonomous driving applications. The sensors used in the data set do not record the data synchronously. This creates an additional ambiguity in the input data between the position information inferred from the lidar and from the radar data. The network training should compensate for this effect partially; however, we expect the precision of the fusion to increase when synchronized sensors are available. This paper focuses on an approach for object detection. Tracking/prediction is applied as a late fusion scheme or as a subsequent processing step to the early fusion. In contrast, LiRaNet [44] performs a combined detection and prediction of objects from the sensor data. We argue that condensed scene information, such as object and lane positions, traffic rules, etc., are more suitable for the prediction task in practice. A decoupled detection, tracking, and prediction pipeline increases the interpretability of all modules to facilitate validation for real-world application in autonomous driving.