Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10× finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.

Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.