Vehicle Instance Segmentation and Prediction in Bird's-Eye-View for Autonomous Driving

Engineering notes

Key topics: autonomous driving system, autonomous driving, bev, end-to-end, semantic segmentation, instance segmentation, nuscenes, perception, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Joint perception and prediction methods predict the segmentation of traffic participant instances and their future movements under the BEV spatial representation, using raw sen-sor inputs in an end-to-end manner. However, existing research still faces the problems of large model volume and slow inference speed, which cannot meet the requirements of lightweight and fast autonomous driving systems in practical applications. To ad-dress this issue, this paper implements an efficient and lightweight end-to-end joint perception and prediction model. The model is built based on CNN only and is implemented through two stages of training. The first stage builds a BEV semantic segmentation model to obtain features of the scene of BEV space and various types of traffic participants. The second stage predicts vehicle instance segmentation and future movements based on the BEV features trained in the first stage. Experiments on the nuScenes dataset show that the model in this paper improves the inference speed to approximately two times that of the existing studies and shrinks the number of model parameters to 1/2 without losing too much prediction performance. The ablation experiments validate the effectiveness of each module on the overall performance.

Links and sources

• Official / arXiv page

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