**MotionNet** is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.

**SCARLET** is a type of convolutional neural architecture learnt by the [SCARLET-NAS](https://paperswithcode.com/method/scarlet-nas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The three variants are SCARLET-A, SCARLET-B and SCARLET-C. The basic building block is MBConvs from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). Squeeze-and-excitation layers are also experimented with.

SCARLET

SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search

MotionNet

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution [convolution](https://paperswithcode.com/method/convolution) stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several ($4$ in the paper) stages and
the $n$th stage contains $n$ streams corresponding to $n$ resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.

Source	MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com