Autonomous Driving Systems continue to face significant safety challenges in complex, open, and uncertain real-world road environments. Based on 710 publicly available autonomous vehicle accident reports from the California Department of Motor Vehicles, this study conducts an in-depth analysis of the characteristics and influencing factors of three types of ADS-involved collisions: those with static obstacles, pedestrians/non-motorized vehicles, and moving vehicles, with the aim of constructing typical scenario profiles for these accidents. Taking into account the inherent characteristics of ADS in performing perception-decision-execution tasks, an accident scenario profiling index system is developed across four dimensions： human-vehicle interaction, system characteristics, road conditions, and environmental factors. The research comprehensively employs Multiple Correspondence Analysis to uncover intrinsic relationships among multi-dimensional qualitative variables in accidents. Furthermore, it establishes a typical scenario profiling methodology by integrating Self-Organizing Map (SOM) neural networks with the K-means clustering algorithm to identify high, medium, and low risk factor combinations. Through data mining techniques, the characteristics and influencing factors of collisions involving autonomous vehicles with static obstacles, pedestrians/non-motorized vehicles, and moving vehicles are deeply analyzed, leading to the construction of typical accident scenario profiles. The data indicates that 73.94% of the vehicles involved in accidents had been in service for less than two years, 77% of the accidents occurred on roads without physical separations, and over 50% took place at intersections. The analysis of typical scenarios reveals both similarities and differences in the dominant factors and scenario characteristics across the three accident types. Firstly, significant influencing factors common to collisions with all three types of objects include the decision-making algorithm (rule-driven and dual-driven) and the perception system (vision-dominant and radar-dominant). However, typical scenarios for collisions with static obstacles involve lighting conditions and human driving modes; those with pedestrians/non-motorized vehicles are often related to the type of autonomous vehicle control architecture; while collisions with moving vehicles primarily involve the regional environment. Ultimately, the study precisely delineates refined profiles for the three types of typical scenarios, providing crucial data support and a theoretical basis for the testing, optimization, and safety risk assessment of Autonomous Driving Systems, thereby contributing to the enhancement of the overall safety level of autonomous driving technology.