A method to keep autonomous vehicles steadily drive based on lane detection. (29th March 2021)

• Record Type: Journal Article
• Authors: Wu, Zhenyu; Qiu, Kai; Yuan, Tingning; Chen, Hongmei
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 2(2021)
• Publication Date: 2021-03-29
• Subjects: Autonomous driving -- decision-making -- lane detection -- LSTM; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298814211002974
• Languages: English
• ISSNs: 1729-8806

Abstract

Existing studies on autonomous driving methods focus on the fusion of onboard sensor data. However, the driving behavior might be unsteady because of the uncertainties of environments. In this article, an expectation line is proposed to quantify the driving behavior motivated by the driving continuity of human drivers. Furthermore, the smooth driving could be achieved by predicting the future trajectory of the expectation line. First, a convolutional neural network-based method is applied to detect lanes in images sampled from driving video. Second, the expectation line is defined to model driving behavior of an autonomous vehicle. Finally, the long short-term memory-based method is applied to the expectation line so that the future trajectory of the vehicle could be predicted. By incorporating convolutional neural network- and long short-term memory-based methods, the autonomous vehicles could smoothly drive because of the prior information. The proposed method is evaluated using driving video data, and the experimental results demonstrate that the proposed method outperforms methods without trajectory predictions.