GNSS-Based Lane Keeping Assist System Using Model Predictive Control and Time Delay Compensation

In recent decades, 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 be equipped with some 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 or blurred lane markings. To overcome this limitation, we have developed Global Navigation Satellite System (GNSS)-based LKA systems, which are not affected by the surrounding environment such as weather and the condition of lane markings. 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 with MPC had low robustness regarding the time delay of a GNSS and that countermeasures for the time delay were necessary. In this paper, we apply Smith predictor-like Time Delay Compensation (TDC) to compensate for the time delay. The TDC predicts the current state variables from the past sensor signals based on the vehicle dynamics. We demonstrate that the TDC stabilizes the LKA system even when the GNSS has a time delay in a simulation. Furthermore we add another TDC to compensate for the time delay of Electrical Power Steering (EPS) with the aim of reducing the oscillation of the steering wheel angle. Finally, we evaluate the lane keeping performance in a real-vehicle experiment on a snow-covered highway.