## 1 Introduction  
Autonomous vehicle technology has become a highly involved focus in intelligent transportation systems [1]. With the rapid development of intelligent and connected technology, people have high expectations for the high-level autonomous vehicle, which can be used to build a more intelligent, efficient and safe road-traffic system [2, 3]. However, scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous scenarios, such as ramp merge in and out, autonomous parking and intersection crossing. The road facilities, traffic environment, and weather conditions in the above scenarios are complex and changeable, such as roundabouts, unexpected pedestrians, and running the red light, which puts high demands on the perception, decision-making, and planning capabilities of the automatic driving system [4, 5]. Currently, research, development, and verification of autonomous vehicles primarily focus on highway sections and straight urban roads [6]. However, intersections are widely recognized as the most hazardous sections of road due to their complex traffic environment,-frequent traffic signals, and prevalence of traffic violations [7]. Therefore, it is necessary to explore vehicle safety in intersection traffic more extensively. Statistics from the United States indicate that over 30% of reported crashes and 50% of fatal and injury-causing crashes occur at intersections [8]. Implementing autonomous driving functions in intersection areas presents greater challenges compared to highway and urban straight-road sections [9]. In straight road sections, the autonomous driving system can achieve comprehensive scenario recognition and the development and testing of corresponding safety functions based on its intelligent perception system and the path-planning algorithm with lane-lines assistance [10]. Furthermore, on straight road sections, road traffic participants typically travel in the same or opposite directions, and the level of traffic flow complexity is relatively low. Due to the shared driving feature of the intersection area, vehicle intersection traffic faces four major challenges: firstly, it is impossible to set physical lane lines for the traffic participant trajectory [11]; secondly, the autonomous vehicle will highly rely on single-vehicle sensing capabilities in the intersection, which is limited by the field of view (FOV) of its own perception system, especially when obstacles are present, and therefore, the collected environmental information has limitations, making it difficult to ensure high reliability and traffic safety for intersection traffic [12]; thirdly, the probability of unpredictable traffic behavior and violation of traffic regulations in the intersection area is high, especially for pedestrians and non-motorized vehicles [13]; fourthly, the number of incoming road lanes is inconsistent with that of outgoing road lanes in an intersection section, and the multitude of trajectory choices poses a crucial consideration for the automated vehicle, presenting further challenges in decision planning capabilities [14]. The above problems make it more dangerous for traffic participants to perform hazardous and unpredictable maneuvers at intersections, especially in traffic congestion. In this paper, a proposed method was evaluated using the naturalistic driving dataset DAIR V2X, which is the first large-scale, real-world, and sequential V2X dataset. Furthermore, the effects of various factors on vehicle dynamic-behavior parameters were used to represent driving behavior characteristics and establish vehicle driving-behavior boundaries for intersection traffic optimization. The test and evaluation of the vehicle driving-behavior boundary have been carried out using real-life naturalistic driving data. The paper is organized as follows: firstly, a brief review of typical intersection-traffic research methods is introduced in Section 2.