## 1 Introduction  
With the rapid progress of electrification and automation in the automotive industry, vehicles have experienced significant advancements in their automation levels and ease of operation [1]. This has made their operation and control more accessible for human drivers. However, the number of traffic accidents caused by driver violations or improper vehicle handling on the roads has not shown a significant decline [2]. While advanced driver assistance systems (ADASs) and other autonomous technologies can mitigate the risk of collisions to some extent, they cannot entirely compensate for the deficiencies in environmental perception and behavioral execution exhibited by some drivers [3, 4]. To enhance overall road safety in urban environments, it is essential to address the issue of driver capabilities [5]. This involves improving the standards for driver competency testing and capability assessment, selecting individuals who are genuinely prepared for safe road operation, and eliminating those who pass tests primarily due to exam-oriented preparation. China, as a nation with a substantial number of driver examinations each year, currently relies on a standardized and formulaic evaluation system to expedite driver competency testing. The existing Subject 3 examination employs a machine that uses a simple base station GPS for vehicle positioning, roadside RSUs (roadside units) for real-time transmission of traffic signals and road signs status, and pre-established points on a fixed course to evaluate candidates. While this system can identify some driver errors resulting from improper operations, such as driving over lane boundaries or running red lights, it still requires the presence of a safety judge in the front passenger seat of the vehicle to assess potential conflicts with the environment. The safety judge checks adherence to qualified driver norms and driver distractions. Sometimes, their evaluation is subjective and may be subject to external influences, like personal interactions and dealings, rendering the Subject 3 examination less intelligent and standardized. Furthermore, the current Subject 3 examination consists of individual sub-tests with specific objectives, and candidates often perform predefined actions on familiar road segments. Some relatively straightforward skills, such as high-speed vehicle control, are assessed in overtaking exercises, while adjustments to vehicle angles during movement are evaluated in turning exercises. This compartmentalized approach not only oversimplifies the comprehensive evaluation of a driver's abilities but also lacks rationality and transparency. In response to the existing exam-oriented and non-theoretically validated Subject 3 examination, this paper proposes a de-subproject, scenario-based, and de-processed driver road performance assessment approach with an in-vehicle and external vehicle integrated perception system. We conducted an examination of road testing methods and characteristics in other international regions. The prevailing driver competency assessment systems [6,7] predominantly employ statistical approaches for testing, posing significant challenges in practical exam application. This limitation renders them ineffective in the context of the extensive scale of driving examination demands, such as those encountered in China. Our approach is characterized by its intelligent assessment ability and considers a driver's environmental perception and behavioral capabilities in both routine and high-risk emergency scenarios, aided by high-precision maps and information from inside vehicles. Its aim is to replace the current China Subject 3 evaluation process to enhance the assessment of drivers' real capabilities for road operation. This article is organized as follows: We comprehensively outline the derivation process and application methods of the proposed intelligent driver RPAM in the second section.