The active scene flow estimation technology aims at higher quality of scene flow data through actively changing the ego-vehicle’s trajectory as well as sensor observation position. The major challenges for active 3D point cloud scene flow estimation for autonomous driving (AD) include mis-registrations of point cloud frames, inaccurate scene prediction, and inefficient scene similarity evaluation. This paper presents a framework of active scene flow estimation for AD applications, which consists of three main modules: robust scene flow estimation, reliable reachable area detection, and efficient observation position decision. The novelty of this work is threefold: 1) developing a robust bi-direction attention-based mechanism neural network scene flow estimation method; 2) developing a reliable reachable area detection strategy through hidden points removing (HPR) based scene prediction and a legality checking scheme between the ego-vehicle and the road as well as the predicted scene; 3) developing an efficient observation position decision strategy through building a scene similarity measure, which can help evaluate the differences between two frames of point clouds from different views. The proposed method is trained and verified using a CARLA simulator based dataset, the FlyingThings3D and KiTTI datasets, which have the accurate scene flow ground-truth. The experiment results demonstrate the superior estimation performance and generalization capacity of our method for various AD scenes as well as different system configurations, compared with other state-of-the-art (SOTA) methods.
Active Scene Flow Estimation for Autonomous Driving via Real-Time Scene Prediction and Optimal Decision
IEEE Transactions on Intelligent Transportation Systems ; 25 , 6 ; 5997-6012
2024-06-01
3997734 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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