# Performance Evaluation of a Lane Correction Module Stress Test: A Field Test of Tesla Model 3
## 6 Conclusions and Outlook  
It can be deduced that the issue discovered by this stress test can be linked to industry software development practices. Additionally, field testing was the best option for the cyber-physical system approach to this experiment because these vehicles are designed for human drivers, who can have medical complications that might require lane correction to be as consistent as possible until either FSD is activated and drives the car to safety or some other solution the company can devise for such eventualities. Therefore, the research design and methodology established will allow for data capture and collection for analysis at the post-testing stage of the project. Real-world data on lane correction or any other driver-assist systems are not as well defined as needed for comparing to results captured by a research team, which are usually specific and narrow, and expose issues that are particular to a vehicle make or model. The National Highway Traffic Safety Administration (NHTSA) said that the first-of-its-kind data specific to traffic accidents that were acquired in 2021 do not yet have proper context and are only meant to be a guide to quickly identify potential defect trends and help determine whether the systems are improving the safety of vehicles [21]. Nonetheless, the fatal error found in this experiment can be reasonably regarded as a potentially dangerous problem for traffic safety. An experiment conducted in a real-world environment has some advantages and disadvantages. The disadvantage is the inability to control the environment. The advantage is that we were testing the vehicle in its natural environment, and any error that presents itself will be evident to the test driver. A physical anomaly can be detected and corrected. The main concern for this study is the careful introduction of advanced technology where the worldwide average of traffic accidents does not go up when it should decrease. For example, in Japan, 38.9% of single-vehicle crashes involve unintentional lane departure. In the USA, the road departure problem accounts for 41% of all vehicle fatalities [22]. If a lane correction module is not consistent, how would a fatal error that occurs randomly contribute to accidents? The progress made in this study is a significant beginning of a scientific method in experimental test driving in automotive computer technology, yet this is specific to hyperconnected vehicles, whether electric or hydrogen. Vehicular systems are mostly regulated and controlled by computer functions and applications. Any autonomous driving controller requires accurate sensory information as its input. To meet the requirements, sensors must go through a complex offline calibration process to transfer the raw data in the sensor frame to meaningful data in the vehicle frame [23]. The importance of stress testing vehicles, even if they have come off the assembly line, might not have been adequately recognized for fatal errors. All of the propositions for environmental control and the amount of testing will require testing beyond the constraints of this stress test. Despite this being an early stage in the research, we have set a guideline for further research in a controlled setting where the possibility of interference in real-world traffic is nullified and extraneous variables are reduced significantly. Regardless, the error discovered is significant, even if the issue is in select Tesla models, trims, or years instead of across the entire fleet. This study did not have access to Tesla's internal data sets, source code, or vehicular performance testing results concerning the lane correction model within the Autopilot Suite. Regardless, this study should provide the vehicle manufacturer with significant feedback on new methods of discovering errors in performance features that can be discovered during the use of the vehicle in a real-world setting. This study's proposed solution for a fix is a software update after a code review.