Abstract
Among surrounding information-gathering devices, cameras are the most accessible and widely used in autonomous vehicles. In particular, stereo cameras are employed in academic as well as practical applications. In this study, commonly used webcams are mounted on a vehicle in a dual-camera configuration and used to perform lane detection based on image correction. The height, baseline, and angle were considered as variables for optimizing the mounting positions of the cameras. Then, a theoretical equation was proposed for the measurement of the distance to the object, and it was validated via vehicle tests. The optimal height, baseline, and angle of the mounting position of the dual camera configuration were identified to be 40 cm, 30 cm, and 12°, respectively. These values were utilized to compare the performances of vehicles in stationary and driving states on straight and curved roads, as obtained by vehicle tests and theoretical calculations. The comparison revealed the maximum error rates in the stationary and driving states on a straight road to be 3.54% and 5.35%, respectively, and those on a curved road to be 9.13% and 9.40%, respectively. It was determined that the proposed method is reliable because the error rates were less than 10%.
Highlights
Driving automation, as defined by the Society of Automotive Engineers (SAE), is the internationally accepted standard
Applicability evaluation through real vehicle tests using the optimal camera position determined in step 3 and the distance calculation equation proposed in step 4
TheResults vehicle test was conducted corresponding to four cases of stationary and driving test was corresponding to four the cases of stationary and driving statesThe on vehicle the straight andconducted curved roads
Summary
As defined by the Society of Automotive Engineers (SAE), is the internationally accepted standard. SAE provides taxonomy with detailed definitions for six levels of driving automation. Mass-produced vehicles have recently begun to be generally equipped with Level 2 autonomous driving technology. This technology provides drivers with partial driving automation and is called advanced driver assistance systems (ADAS). Experimental evaluation of algorithm precision according to three variables for optimal dual-camera positioning. Proposal of equations for calculating the distance between the vehicle and objects in front of it in straight and curved roads for test evaluation. Applicability evaluation through real vehicle tests using the optimal camera position determined in step 3 and the distance calculation equation proposed in step 4. 2. Theoretical Background for Dual Camera-Based Image Correction and the Proposed
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