# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 7 Challenges
### Uncertainty in Radar Detection  
Learning-based radar detection shows its potential in classifying different road users. However, the performance evaluated on research datasets could be biased due to class imbalance and simple scenarios. [PERSON] et al. (2018) summarised some failure cases for radar detection in the VoD dataset: Two close pedestrians can be detected as one bicyclist. One large object, for example a truck or a bus, can be split into two smaller ones. Distant objects with few reflections may be missed by the detector. Strong reflections from metal poles and high curbs can mask real objects. Most of these failures come from the imperfection of radar sensors with respect to angular resolution and dynamic range. To make it worse, neural networks tend to be overconfident in their incorrect predictions ([PERSON] et al., 2017). For autonomous driving, the misspecified confidence in perception can leak to downstream tasks such as sensor fusion and decision making, potentially leading to catastrophic failure. [PERSON] et al. (2018) investigated the class uncertainty of a learning-based radar classifier under different perturbations, including domain shift, signal corruptions, and out-of-distribution data. Experiments indicate their baseline network are severely over-confident under these perturbations. There are two kinds of uncertainty: Data uncertainty, also known as aleatoric uncertainty, is caused by noisy input. Model uncertainty, also known as epistemic uncertainty, is caused by insufficient or inappropriate training of the network. Sources of model uncertainty include three cases: covariate shift (\(p(x)\) changes), label shift (\(p(y)\) changes), and open set recognition (unseen \(y\)) ([PERSON] et al., 2017). The sum of data uncertainty and model uncertainty is referred to as predictive uncertainty. For the task of probabilistic object detection ([PERSON] et al., 2017),uncertainties of two parameters are of interest: class uncertainty, which encodes the confidence in the classification, and spatial uncertainty, which represents the reliability of the bounding boxes. Class uncertainty can be seen as model uncertainty, while spatial uncertainty is more relevant to data uncertainty introduced by noisy input. For classification tasks, the simplest way is to learn a function that maps the pseudo probability output by the softmax layer into the true probability. The true probability is defined as the classwise accuracy on the training set. This process is usually called network calibration. Since it is a postprocessing method, both the model size and inference time are not affected. The calibration method is mainly concerned with the aleatoric part of the overall uncertainty [192]. To calibrate the radar classifier, [PERSON] et al. [206] compared different postprocessing techniques, including temperature scaling [202], the latent Gaussian process (GP) [207], and mutual information maximisation [208]. Mutual information maximisation achieves the best balance between performance and inference time. Some recent research indicates that soft-label augmentation techniques, such as label smoothing [209] and mixup [210; 211], can effectively mitigate the over-confidence problem, thus helping network calibration. [PERSON] et al. [206] suggest the use of label smoothing regularisation in radar classification. The core idea is that the classifier should give lower confidence to distant objects with low received power. Therefore, they proposed two label smoothing techniques to generate soft labels according to the range and the received power. respectively. Experiments showed that both of them can significantly improve the calibration performance. In addition to calibrating the class uncertainty, we are also interested in estimating the spatial uncertainty in bounding box regression. Monte Carlo dropout [212] and deep ensembles [213] are popular in estimating predictive uncertainty. However, experiments [214] show that these methods only provide marginal improvements in object detection, but at a high cost.