# Performance Evaluation of a Lane Correction Module Stress Test: A Field Test of Tesla Model 3
## 5 Experimental Results
Cumulatively, the error rate ratio yielded for the test is 4.41, and the error rate is 23%. Individual calculations for each error event yielded 5 instances of 100 % failure of the lane correction module out of 12 error instances. In other words, there were five instances where failure was encountered on the first try. The accuracy rate of the lane correction module is 77.4% cumulatively, which is a failure. When the error events are broken down individually, 11 out of 12 events fail. The standard deviation is 4.6, and the confidence interval is 1.32. Notes from the field test confirmed that there were multiple events where an error occurred on the first instance of the car touching the right solid lane line. It was also noted that testing the module on the left-hand side of the road (American), in which a perforated line is present on a two-lane road, was not tested at the same frequency as the right-hand side of the lane was tested because of safety reasons. It proved too dangerous to test without a controlled road environment. However, it seems like the lane correction module fails to detect the left sideline during a two-sided bounce from right to left. It was successful only once. In one of the error events, it was observed that there was a crossroad intersection (where the sideline disappeared) that was passed during the test. It is unclear if that contributed to the error, as errors occured in a variety of road positions and conditions. There were two instances on the test where the vehicle bounced from the right to the left lane and failed to correct itself on the left lane side. The speed varied around the limit set by the experiment requirements, yet the car's speed did not seem to impact the outcome in prior observations during initial practice runs. The conclusion for the failure of the lane correction module is based on the numerical analysis demonstrating the randomness of the error event and from the perspective of a driver depending on consistent operational behavior from a vehicular system. The strict scientific method applied to the experiment gives an observable structure for engineers attempting to solve the problem and allows for an objective view of the technical issue without the burden of brand loyalty or commercial popularity. There is a comparison test that the Human and Autonomy Laboratory conducted at Duke University in 2020, where they conducted field tests on multiple system modules for the reliability of driver-assist functions. The lane correction module was one of the features of the ADAS system that was tested. The conclusion of their test, which was cumulatively compared to our singular and practical focus on the lane correction module, is that the performance of the computer vision systems was extremely variable. This variation was likely responsible for some, but not all, of the delays in alerting a driver whose hands were not on the steering wheel [20]. Their conclusion on Model 3, focusing on the software and hardware performance of the computer vision system version at that time, aligns with the results and conclusion found in this experiment. Yet, our method of stress testing lane correction was designed exclusively for the component instead of a cumulative test on multiple systems.