SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios

TL;DR

SSF-PAN introduces a novel neural network for semantic scene flow segmentation that enhances autonomous navigation in traffic scenarios by improving detection accuracy and computational efficiency without relying on traditional map-based methods.

Contribution

The paper presents a new neural network for semantic scene flow segmentation, an iterative optimization framework, and a scene flow-based navigation platform, advancing perception capabilities for autonomous vehicles.

Findings

Outperforms traditional methods in scene flow accuracy

Achieves higher moving object detection precision

Demonstrates improved computational efficiency and navigation performance

Abstract

Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which suffer from low computational efficiency and accuracy. The proposed SSF-PAN can achieve the functionalities of LiDAR point cloud based object detection/localization and SLAM (Simultaneous Localization and Mapping) with high computational efficiency and accuracy, enabling map-free navigation frameworks. The novelty of this work is threefold: 1) developing a neural network which can achieve segmentation among static and dynamic objects within the scene flows with different motion features, that is, semantic scene flow (SSF); 2) developing an iterative framework which can further optimize the quality of input scene flows and output segmentation results; 3) developing a scene flow-based navigation platform which can test the performance of the SSF perception system in the simulation environment. The proposed SSF-PAN method is validated using the SUScape-CARLA and the KITTI datasets, as well as on the CARLA simulator. Experimental results demonstrate that the proposed approach outperforms traditional methods in terms of scene flow computation accuracy, moving object detection accuracy, computational efficiency, and autonomous navigation effectiveness.