RDAS-MODEL: An Efficient Model of Road Drivable Area Segmentation for Autonomous Driving

Abstract The drivable area segmentation technique plays a crucial role in ensuring the safe driving of autonomous driving vehicles in complex environments. However, in realistic drivable area segmentation scenarios, the presence of complex and high uncertainty factors such as lighting differences and weather makes accurate drivable area segmentation still challenging. To reduce the impact of these uncertainties on the real scene, and improve the accuracy of the drivable area segmentation, this study proposes an efficient road drivable area segmentation model (RDAS-MODEL). Firstly, a multi-scale feature adaptation module (MFAM) is designed and integrated into the backbone network, which aims to reduce the uncertainty of the feature map, and thus effectively improve the accuracy of the drivable area segmentation. Next, the high and low feature interactive pyramid network (HLFIPN) is introduced, which can enhance the interaction between shallow and deep features, thus improving the segmentation of multi-scale objects in complex scenes. Then, the redesigned shared convolutional segmentation head (SCSH) can reduce the number of parameters and computation, and also ensure the real-time performance of the model, which further improves the segmentation performance. Finally, the adaptive training sample selection (ATSS) strategy is utilized to balance positive and negative samples, effectively minimizing object misdetection and omission. Experimental results based on the BDD100K dataset indicate that the mean Average Precision (mAP)mask@50 and mAPmask@50:95 of the proposed model on the drivable area are 95.2% and 77.2%, respectively, and the real-time inference speed is 102 frames per second (FPS), which significantly exceeds the performance of some advanced methods. Experiments are also conducted on the UAS dataset to further demonstrate the generalization ability and effectiveness of the method. In addition, the qualitative results of experiments on real campus scenes suggest that the proposed method is well adapted to real scenes and has a high segmentation accuracy. The results of a large number of experiments conducted on two public datasets and self-captured images all show that the proposed method can effectively improve the segmentation accuracy of the drivable area, providing a new idea for accurately segmenting the drivable area.