Real-time Multiple Path Prediction and Planning for Autonomous Driving aided by FCN

Abstract

Real-time path planning is the core of autonomous driving. It plans the optimal drive path for the autonomous vehicle (AV) based on concurrent perception and localization information. Due to the uncertainties in those information, it is necessary to plan multiple paths in real-time for selection, which, however, is expensive for traditional graph search methods. In recent years, path learning methods based on semantic segmentation proved that the driving area of an AV can be predicted by learning perception information and its historical paths. Inspired by these methods, this paper proposes a real-time multiple path planning method combining semantic segmentation with the traditional graph-based search. A fully convolutional neural network (FCN) was first designed to learn the optimal path area generated by an A* based path planning method in various real and simulated environments. By injecting noises into localization information, the generalization ability of the neural network is greatly enhanced facing inaccurate localization results. Then, multiple possible path areas inferred by the FCN are adopted as constraints for the following A* based path planning. The results showed that the optimal path area reduces the computational cost of subsequent path searches, which makes real-time multiple path planning possible. Besides, even if the neural network infers an unreasonable path area, the A* based path search method can still guarantee the correctness of the path searching.