Enhancing autonomous driving decision-making using knowledge graph representation and distillation techniques

The rapid evolution of autonomous driving technology hinges on advanced perception systems, which are integral for safe and efficient vehicle navigation. While effective, traditional approaches often lack semantics, preventing their application in real use cases. To address this challenge, we propose an innovative approach combining semantic representations and integration of semantic artefacts with distillation techniques. We integrate domain semantic artefacts to create a knowledge graph that provides a structured framework of the driving environment, including dynamic elements like pedestrian behavior. This integration enables the vehicle to accurately interpret and react to various events, particularly in complex urban scenarios. Further enhancing this system, we distill knowledge from a complex, pre-trained segmentation model into a more lightweight counterpart to have a more robust approach, achieving a streamlined segmentation process required in real-time autonomous driving systems. A focal point of our study is a case analysis of pedestrian detection at crosswalks, a critical aspect of urban driving. The results showcase the efficacy of our framework, elevating the decision-making process in autonomous vehicles.