Vision-based integrated lateral control system for electric vehicles considering multi-rate and measurable uneven time delay issues

Driver assistance systems such as automatic steering for lane keeping are of particular importance for vehicle´s lateral safety, and onboard look-ahead cameras are widely employed to acquire surrounding information for realization of such applications. In fact, vision-based lane keeping and automatic steering have been intensively studied in the past few decades. Meanwhile, electric vehicles, as green solutions for future transportation, are gaining increasing attentions nowadays. From the viewpoint of motion control, differential torque between left and right in-wheel-motors is considered as an effective method for lateral safety control of electric vehicles. Nevertheless, the sampling rate of normal cameras is 30 Hz, which is much slower compared with that of motors and other kinds of onboard sensors. Moreover, image processing brings uneven time delay (which is usually measurable) into the visual signals, which further complicate the system sampling sequence. Many previous research simply adapt the whole system´s sampling frequency to the camera; however, the held and random delayed feedbacks deteriorate system performance and may cause instability. In this paper, the two problems are solved by a multi-rate Kalman filter with random measurement delays compensation using a residual estimation technique.