A Study on the Evaluation Method of Autonomous Vehicle for Fixed Targets

Abstract

Abstract Recently, the automobile industry aims to commercialize autonomous vehicles, standardization and research and development are actively underway based on the specifications of automotive electronic control systems and the verification of functional errors. Based on this, autonomous driving technology is becoming more advanced, and it is preparing for an era of full autonomous driving through V2X technology convergence. It is also, expanding the models of ADAS applied vehicles such as FCW, BSD, LCA. Based on this, research is also actively underway to secure safety of vehicles and pedestrians, such as V2X, Localization, Fail Safe, to supplement the limitations of sensor-based autonomous driving. In this regard, this study proposes a theoretical formula for longitudinal relative distance computation for autonomous vehicles evaluation method, and uses test device such as DGPS to collects and verify data. In addition, a scenario was proposed for the fixed target, based on this, four types of test were formed to conduct the actual test, and the relative distances were compared, analyzed and verified. Comparative analysis results, in the second test of the first test scenario, the avoidance test of the fixed target in driving own lane, the minimum error rate was 0.5%, and in the second test of fourth test scenario, the avoidance test of the fixed target in driving own lane, the maximum error rate was 7.4%. The main cause of error is, it was judged as an error due to sensor recognition, depending on the scenario progress method, the condition of the test path, and the weather such as sunlight. In the future, we plan to conduct an evaluation on moving target.