Lane keeping assist system (LKA) alerts the driver or intervenes in the driving when the vehicle deviates from the lane. But its effect is highly dependent on the driver's acceptance. Distance to Lane Crossing (DTLC) and Time to Lane Crossing (TTLC) are two important factors to consider the danger level of the scenario, which are also two references for drivers to make decisions. At present, most of the functional design standards are based on these values, while they often differ for different vehicle movements. This study uses a driving robot to precisely control the test conditions and performs field tests on two advanced autonomous vehicles in National Intelligent Connected Vehicle (Shanghai) Pilot Zone. The test conditions are extended based on various test standards and the LKA performance of vehicles in the pre-experiment. The application of high-precision maps and RT systems in the test provided positioning information for the driving robot with an accuracy error of less than 2 cm. The FIR filtering of the audio and video data is used to obtain the driver's feedback on the alarm or intervention. According to the UTC time synchronization, the thresholds of the DTLC and TTLC at that moment are obtained. Finally, the Kalman Filtering and one-way ANOVA method is used to obtain the DTLC or TTLC distribution of the vehicle movement status characteristics. This result can be used to compare with the analysis of natural driving behavior, and then apply in proposing a more reasonable Human Machine Interaction (HMI) scheme considering the driver's operating characteristics.