Proactive Scenario-driven Adaptable Defensive Driving Architecture for Autonomous Driving Environment

The revolutionary development of artificial intelligence is making significant advancements such as Gen AI, and safe autonomous driving technology systems are being researched to be applied to autonomous driving environments that require the application of advanced and reliable technologies. In order to establish a stable autonomous driving environment, object detection, motion prediction are being researched. Currently, road marking recognition is performed in the object detection field to acquire road driving environment information, however, the inference of possible situations and proactive response from road markings are not being conducted. To overcome the limitations, this paper proposes a Proactive Scenario-driven Adaptable Defensive Driving (PS-ADD) Architecture to infer Environment Adaptive Scenario from proactive scenarios and derive Environment Adaptive Preemptive Plan. To infer proactive scenarios, road markings and real-time object information is combined to generate Proactive Driving Approaches.Then, Environment Adaptive Defensive Decision Making (EADDM) algorithm enables Adaptable Defensive Decision Making by deriving realtime environmentally adaptive scenarios and preparing adaptive preemptive plans. PS-ADD Architecture enables to predict and handle potential scenarios in advance in complex driving situations, allowing for stable defensive driving in unanticipated situations.