# Research on Driving Scenario Knowledge Graphs
## 1 Introduction  
In recent years, significant progress has been made in the field of autonomous driving, largely attributed to the development and application of artificial intelligence technologies, particularly algorithms such as deep learning and reinforcement learning. These algorithms play crucial roles in perception, decision-making, and control, enabling vehicles to accurately perceive and adapt to their surrounding environments. Despite remarkable achievements, the comprehensive application of advanced autonomous driving technologies still faces numerous challenges. This is mainly due to the limitations of artificial intelligence algorithms in interpretability, understanding complex scenarios, and handling unknown situations [1]. When faced with real-world situations that differ from the training data, current artificial intelligence algorithms may encounter significant setbacks, potentially leading to accidents involving autonomous vehicles. For instance, several fatal accidents involving Tesla's autonomous driving vehicles can largely be attributed to failures in their perception systems [2, 3]. Similarly, the fatal collision between an autonomous Uber vehicle and a pedestrian also exposes shortcomings in autonomous driving in terms of perception and prediction [4]. Additionally, artificial intelligence algorithms rely on vast training datasets, requiring them to process large volumes of example data to achieve high accuracy, which sharply contrasts with the efficiency of humans in integrating new information through single instances of learning. In this context, understanding context becomes particularly important for agents to handle unknown situations, provide feedback to users, and understand their own functionality. Scene understanding requires systems to possess a profound semantic understanding of entities and their relationships in complex physical and social environments. To meet thisrequirement, a feasible approach is to represent entities and their relationships in scenes as knowledge graphs (KGs) [5]. By constructing scene knowledge graphs, systems can better predict entity behaviors, thereby enhancing scene understanding capabilities [6; 7]. For example, consider a scenario where an autonomous vehicle is navigating through a complex intersection. Traditional visual perception systems may identify traffic signals, pedestrians, and other vehicles. However, relying solely on this information may not accurately predict the next actions in certain situations. If the system can leverage knowledge graphs to understand the structure and rules of the intersection, it can improve predictions. It can identify relationships between traffic signals and pedestrians, as well as the traffic rules among different vehicles. This enables more accurate predictions of when to stop, when to turn, or when to accelerate through. This knowledge-injection-learning-based approach can compensate for the limitations of traditional visual perception systems. It improves the performance and safety of autonomous driving systems in complex scenes. Therefore, knowledge graph construction based on knowledge injection learning holds tremendous potential in addressing the complex technical challenges of scene understanding in autonomous driving. However, the application of knowledge graphs also faces a series of challenges and issues, such as the cost of knowledge acquisition, the uncertainty of knowledge representation, and the timeliness of knowledge updates. Among these, the cost of knowledge acquisition is a significant consideration factor because building and maintaining a complete and accurate knowledge graph require substantial human and time investments. Additionally, the uncertainty of knowledge representation poses another challenge because real-world knowledge is often fuzzy, uncertain, and may evolve over time and in different environments.