GNSS-based Lane Keeping Assist System Using Model Predictive Control and Time Delay Compensation 2020-01-1023
During the past decades, the research and development in the field of autonomous vehicles have rapidly increased throughout the world, and autonomous driving technologies have begun to be applied to mass production vehicles. Especially recently, even affordable mass production vehicles have begun to carry on some of autonomous driving systems such as a Lane Keeping Assist (LKA) system. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage and blur lane markings. To overcome this limitation, we have developed Global Navigation Satellite Systems (GNSS)-based LKA systems, which are not affected by surrounding environment such as weather and lane markings’ condition. In our latest study, we applied Model Predictive Control (MPC) to our GNSS-based LKA system so as to enhance lane keeping performance. We then revealed that the GNSS-based LKA system using MPC had low robustness to the time delay of a GNSS and that counterplans for the time delay were necessary. In this paper, we apply a smith predictor-like Time Delay Compensation (TDC) to compensate the time delay. The TDC predicts the current state variables from the past sensor signals based on the vehicle dynamics. We exemplify that the TDC stabilizes the LKA system even if the GNSS has a large time delay in a simulation. Furthermore we evaluate the lane keeping performance in a real vehicle experiment on a snow-covered highway.