*5.3. Test Results*

The vehicle test was conducted corresponding to four cases of stationary and driving states on the straight and curved roads. Figure 17 depicts the post-correction images captured by the dual-camera setup used to measure the distance to the object in front of the vehicle. Table 6 summarizes the deviations of the theoretically calculated distances from the actual distances.

(**a**) (**b**)

**Figure 17.** Test result images: (**a**) on the straight road, (**b**) on the curved road.




**Table 6.** *Cont.*

In the case of the stationary state on the straight road, the objects placed at various distances in front of the vehicle were identified. The maximum error was observed to be 3.54%, corresponding to the 30 m point.

In the case of the driving state on the straight road, the objects at distances of 10 m and 20 m in front of the vehicle were identified, whereas those farther away were not. This can be attributed to factors such as vehicular turbulence, variations in illumination, and transmission of vibration to the cameras, caused by the driving state. The maximum error was observed to be 5.35% at the 20 m point.

In the case of the stationary state on the curved road, the objects at distances between 5 m and 20 m in front of the vehicle were identified. The maximum error was observed to be 9.13%, corresponding to the 20 m point.

In the case of the driving state on the curved road, the objects at distances between 5 m and 15 m in front of the vehicle were identified, whereas those at a distance of 20 m were not. Similar to the case of the straight road, this was attributed to factors such as vehicular turbulence, variations in illumination, and transmission of vibrations to cameras. The maximum error was observed to be 9.40%, corresponding to the 6 m point.

The test results demonstrate that the error in the measurement of the distance between the vehicle and objects in front of it increases when the object is detected inaccurately owing to factors such as vehicular turbulence, variations in illumination, and transmission of vibrations to the cameras. Furthermore, the error tends to be relatively large in the case of the driving state on a curved road compared with that on a straight road; this error is affected by the fixed radius of curvature used in the calculation process.

#### **6. Conclusions**

In this study, correction of camera images and lane detection on roads were performed for vehicle tests and evaluation. Furthermore, the mounting positions of cameras were optimized in terms of three variables: height, baseline, and angle of inclination. Equations to measure the distance to an object in front of the vehicle on straight and curved roads were proposed. These were validated via the vehicle tests by classifying stationary and driving states. The results are summarized below:


The test results revealed that the error rate was the smallest (0.86%), corresponding to a height of 40 cm, a baseline of 30 cm, and an angle of 12◦. Hence, this was considered to be the optimal position.

(3) Theoretical equations were proposed for the measurement of the distance between the vehicle and an object in front of it on straight and curved roads. The dual cameras were mounted on the identified optimal positions to validate the proposed equations. Vehicle tests were conducted corresponding to stationary and driving states on straight and curved roads. On the straight road, maximum error rates of 3.54% and 5.35% were observed corresponding to the stationary and driving states, respectively. Meanwhile, on the curved road, the corresponding values were 9.13% and 9.40%, respectively. Because the error rates were less than 10%, the proposed equation for the measurement of the distance to objects in front of a vehicle was considered to be reliable.

To summarize, the mounting positions of the cameras were optimized via vehicle tests using the dual cameras, and image correction and lane detection were performed. Furthermore, the proposed theoretical equation for measuring the distance between the vehicle and objects in front of it was verified via vehicle tests, with obstacles placed at the selected positions.

The aforementioned results are significant for the following reasons. These results establish that expensive equipment and professional personnel are not required for autonomous vehicle tests, enabling research and development focused on facilitating autonomous driving using only cameras as sensors. Furthermore, webcams with easy availability can also be applied without additional sensors to the testing and evaluation of autonomous driving. In the future, we expect tests to be conducted on ACC, LKAS, and HDA at the respective levels of vehicle automation.

**Author Contributions:** Conceptualization: S.-B.L.; methodology: S.-B.L.; actual test: S.-H.L. and B.-J.K.; data analysis: S.-H.L., B.-J.K. and S.-B.L.; investigation: S.-H.L.; writing—original draft preparation: S.-H.L., B.-J.K. and S.-B.L.; writing—review and editing: S.-H.L., B.-J.K. and S.-B.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ministry of Trade, Industry, and Energy and the Korea Institute of Industrial Technology Evaluation and Management (KEIT) in 2021, grant number 10079967.

**Acknowledgments:** This work was supported by the Technology Innovation Program (10079967, Technical development of demonstration for evaluation of autonomous vehicle system) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).

**Conflicts of Interest:** The authors declare no conflict of interest.
