Supervised Object-Specific Distance Estimation from Monocular Images for Autonomous Driving

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8846
• Main Authors: Davydov, Yury, Chen, Wen-Hui, Lin, Yu-Chen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.11.2022; MDPI
• Subjects: Algorithms; Automobile Driving; autonomous driving; Comparative analysis; computer vision; convolutional neural networks; Datasets; Equipment and supplies; Humans; Image processing; monocular depth estimation; Motor Vehicles; Motorcycles; Neural networks; Response rates; Sensors; convolutional neural networks; monocular depth estimation; computer vision; autonomous driving
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22228846

Accurate distance estimation is a requirement for advanced driver assistance systems (ADAS) to provide drivers with safety-related functions such as adaptive cruise control and collision avoidance. Radars and lidars can be used for providing distance information; however, they are either expensive or provide poor object information compared to image sensors. In this study, we propose a lightweight convolutional deep learning model that can extract object-specific distance information from monocular images. We explore a variety of training and five structural settings of the model and conduct various tests on the KITTI dataset for evaluating seven different road agents, namely, person, bicycle, car, motorcycle, bus, train, and truck. Additionally, in all experiments, a comparison with the Monodepth2 model is carried out. Experimental results show that the proposed model outperforms Monodepth2 by 15% in terms of the average weighted mean absolute error (MAE).