Abstract
Three-dimensional vehicle detection using LiDAR point clouds is important for the stability of autonomous driving. It can provide high-quality three-dimensional information for most obstacles. Although efficient algorithms based on a Bird’s Eye View (BEV) feature map have been developed, many research issues still remain; in particular, the existing methods show a limited accuracy in estimating the rotation angle of a 3D object. In this paper, to improve the accuracy of rotation angle estimation, we propose a rotation-aware 3D vehicle detector that extracts distinguishable features from the proposals with various angles of rotations. Experiments are conducted on KITTI dataset and Waymo Open dataset. Our approach improves detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency.
Highlights
Key technologies in self-driving vehicles include environmental awareness, accurate localization, and optimal path planning
The results show that our rotation-aware model can improve detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency
We designed a network that improves the accuracy of rotation angle estimation
Summary
Key technologies in self-driving vehicles include environmental awareness, accurate localization, and optimal path planning. Raises environmental awareness, and drastically improves the safety of unmanned vehicles. Vehicle detection research has been expanded from 2D to 3D. Detection in 3D provides three-dimensional information about an object, including the exact distance from the vehicle to the object. This helps increase the stability of autonomous driving. Accurate threedimensional object information is usually obtained from LiDAR sensors
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