To assess driving safety, there is a need for a scenario-based assessment that is able to reflect various driving situations. When driving safety is secured, an AV can drive harmoniously while complying with traffic laws and minimizing conflicts with the surrounding vehicles. However, research on driving safety, defined as a vehicle recognizing, judging, and reacting to the driving environment on its own in all situations encountered while driving, remains lacking. Crash safety and functional safety can be sufficiently secured through the development of automated driving technologies. To ensure AV safety, it is necessary to secure driving safety among the various AV safety fields. Therefore, for the smooth commercialization of AVs, it is necessary to develop a method to ensure AV safety. In fact, from analyzing the contexts used by newspapers published in Singapore over a period of approximately 67 months, it was found that AV safety, the economy, and the use of personal data were the main issues of debate. AV traffic accident news reports lead to concerns regarding the safety and reliability of automated driving systems (ADSs). ĭespite advances in automated driving technology, AV-related traffic accidents continue to occur. Mercedes-Benz was the first to receive internationally valid certification for a level three automated driving technology, launching the “Drive Pilot” system to support this technology. Recently, the Honda Motor Company introduced the “Legend”, with level three automated driving technology for certain conditions, such as congestion situations. AVs developed by Waymo, Uber, and other companies are also being experimented with, on real roads. The most representative AVs driving on real roads are the Tesla Autopilot and Cadillac Super Cruise. As proof of this, AVs are already driving on real roads in many cities worldwide. As a result, AV technology has grown remarkably. Many companies worldwide are working to develop reliable automated vehicles (Avs). In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. In this context, these various situations are mostly implemented by using systematically developed scenarios. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving.
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