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The requirement of fine-grained perception by autonomous driving systems has resulted in recently increased research in the online semantic segmentation of single-scan LiDAR. Emerging datasets and technological advancements have enabled researchers to benchmark this problem and improve the applicable semantic segmentation algorithms. Still, online semantic segmentation of LiDAR scans in autonomous driving applications remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware, (2) points are distributed unevenly across space, and (3) an increasing number of more finegrained semantic classes. The combination of the aforementioned challenges motivates us to propose a new LiDAR -specific, KNN-free segmentation algorithm -PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points per grid and thus indirectly redistributes the network's attention over the long-tailed points distribution over the radial axis in polar coordination. We find that our encoding scheme greatly increases the mIoU in three drastically different real urban LiDAR single-scan segmentation datasets while retaining ultra low latency and near realtime throughput.

As the complexity of aircraft cockpit operations increases, training effectiveness must be improved, and learning accelerated. Virtual reality (VR) training is increasingly offered as a method for improving training efficacy given its ability to provide a rich sensory experience during learning. This paper describes a study examining how training efficacy can be improved by improving learning diagnostics. We study how varying forms of knowledge assessment are related to different types of task knowledge and task performance in a VR flight simulator. The data suggest that participants who demonstrated higher training comprehension, measured via diagnostic test questions, on conceptual (and to a lesser effect) declarative knowledge, also demonstrated superior knowledge transfer in the VR flight simulator. Findings are discussed in the context of improving cognitively diagnostic assessments that are better able to predict task performance and inform individually tailored training remediation.

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