Correlation Method of Assistance Driving Function and Road Environment Factors in Investigation of Intelligent Vehicle Traffic Accident

Abstract

To address the need for an in-depth exploration of traffic accidents involving intelligent vehicles and to elucidate the influence mechanism of road environment interference factors on both assisted driving systems and human drivers during such accidents, a comprehensive analysis has been conducted using the System-Theoretic Process Analysis (STPA) framework. This analysis focuses on road static facilities, traffic dynamic characteristics, and instantaneous weather conditions in automobile traffic accidents that occur under the human-machine co-driving paradigm with integrated assisted driving functions. Based on these insights, an interference model tailored to road environment factors in traffic accidents of assisted driving vehicles has been constructed.Utilizing recent traffic accident cases in China, the Accident Map (AcciMap) methodology was employed to systematically classify and analyze all accident participants across six levels. Through this rigorous process, 59 accident factors were refined and optimized, culminating in a method for assessing the degree of interference posed by road environment factors in traffic accidents involving assisted driving vehicles. The ultimate objective of this research is to enhance the investigation of road environment interference factors following accidents that occur with diverse assisted driving functions in human-machine co-driving scenarios. By providing a structured and analytical approach, this study aims to support future research endeavors in developing effective traffic accident prevention countermeasures tailored to assisted driving vehicles.

1. Introduction

In recent years, China’s intelligent driving vehicle industry has undergone rapid development, particularly evident in the surge of vehicles equipped with assisted driving functions. According to statistics from the China Automobile and Passenger Association, as of February 2024, the loading rate of new energy passenger vehicles featuring L2 and above levels of assisted driving functions has surpassed 60 percent, with L3-level technology in the process of acceleration. Notably, some L2+ models have already incorporated L3-related functionalities. However, concurrently, traffic accidents involving assisted driving vehicles have become increasingly prevalent. Unlike accidents in aviation, rail, and shipping, investigations into assisted driving vehicle traffic accidents primarily rely on data from vehicle recording systems provided by assisted driving vehicle manufacturers and technology providers to elucidate the course and causes of the accidents. Unfortunately, the lack of thorough investigation and analysis of the road environment involved in these accidents hinders the full restoration of the actual accident sequence.

At present, the investigation of automobile traffic accidents involving assisted driving functions is still in its infancy. SAE International released the recommended guidelines for data sets required for autonomous vehicle accident investigation in 2020 to guide the collection of accident data and improve the design of high-level autonomous driving products [1]. The National Highway Traffic Safety Administration and the National Transportation Safety Board (which began investigating accidents involving Tesla and other assisted driving vehicles in 2021) The focus is on the collision emergency lights of auxiliary driving vehicles under poor lighting conditions, illuminated traffic sign boards and warning piles [2]. Alfredo Garcia [3] studied the applicability of existing road alignment and autonomous vehicles, and analyzed the relationship between the maximum operating speed that can be achieved by the autonomous driving system and the road design speed and curve radius, without considering the impact of road facilities, environment and other factors on the operation safety of assisted driving vehicles. Nastaran Moradloo [4] et al. used unsupervised machine learning methods to find the causes of traffic accidents of autonomous vehicles, and put forward influencing factors such as obstacles, unclear road marking and sudden changes in traffic flow. Guo Yanyong [5] believe that future studies should explore the driver takeover performance evaluation system in the context of human-machine co-driving of networked autonomous driving, so as to conduct refined accident risk causation analysis, traffic safety modeling and evaluation, and accident risk prevention and control strategies and algorithms. Wang Xuesong [6] analyzed the relationship between road alignment design, intersection geometry design and automatic driving perception ability, and found that design indexes such as flat curve radius, chord length and curve deflection Angle affect the visual distance of automatic driving, and road type, flat curve curvature and lane width have a significant impact on automatic driving failure events. The recognition accuracy of automatic driving will be affected by the reflective performance of traffic signs and the setting position, and the failure probability is related to the size of traffic marks, the brightness coefficient of reverse reflection and the extension line. Chen Jiqing [7] based on the accident data in the national vehicle accident in-depth investigation system, selected five scene elements according to traffic environment elements and basic information of test vehicles, analyzed the vehicle traffic accident data through unique coding and cluster analysis methods, and proposed and analyzed the characteristics of dangerous accidents combined with the typical vehicle collision risk scenarios obtained by clustering. Fifteen autonomous driving test scenarios involving road section types were constructed. Scholars mainly focus on the operational reliability of the autonomous driving system at level 3 and above [8], and the perceived stability requirements of the autonomous driving system on road environment elements, etc. [9], and lack of research on the traffic accident investigation of cars equipped with auxiliary driving functions at level 3 [10] and autonomous driving vehicles with partial auxiliary driving functions at level 3 and above [11]. In particular, the relevant contents and methods of the investigation of road environmental disturbance factors [12]. Therefore, how to scientifically and systematically complete the causation analysis of road environment interference factors under the condition that the assisted driving function is opened [13], and support the rapid search of the causation of automobile traffic accidents involving the assisted driving function and the accurate identification of accident responsibility under the man-machine co-driving mode, is a key issue that needs to be solved urgently [14]. This paper focuses on the investigation of traffic accidents involving cars with assisted driving functions enabled in man-machine co-driving mode, encompassing vehicles equipped with assisted driving functions below L3 level and autonomous vehicles with partial assisted driving functions activated at L3 level and above. Utilizing the Systems-Theoretic Process Analysis (STPA) framework, this study analyzes the structure of the vehicle control layer, driving execution layer, and control feedback layer during traffic accidents involving assisted driving vehicles.Drawing upon research on vehicle-related and driver-related investigation items in assisted driving vehicle accidents, and incorporating the findings from the analysis of road environment interference layer within the STPA framework, this paper employs the Accident Map (AcciMap) method to conduct an in-depth exploration of typical traffic accident cases involving assisted driving vehicles. Furthermore, it delves into the investigation of road and environment-related interference factors, such as road static facilities, traffic dynamic characteristics, and instantaneous weather conditions. Correlation analysis is conducted for each element across different levels, clarifying the logical relationships and influence mechanisms both within and between these levels.

2. STPA Analysis of Traffic Accidents of Assisted Driving Vehicles

Factors influencing traffic accidents involving assisted driving vehicles are relatively intricate. Based on the factors associated with vehicles and drivers in previous traffic accidents of assisted driving vehicles, this paper employs the System-Theoretic Process Analysis (STPA) to analyze the road environment factors that contribute to such accidents. These road environment factors can impact not only the decision-making of the assisted driving system and human drivers at the command level, potentially leading to erroneous command actions, but also affect the vehicle control level, resulting in incorrect control actions. Furthermore, they can hinder the proper execution of control actions at the vehicle driving execution level. (Figure 1 included for reference).

3. Analysis of Road Environment Interference Factors

3.1. Content of Road Static Facilities Survey

The investigation content of road static facilities primarily encompasses the examination of basic road information pertinent to traffic accidents involving assisted driving vehicles, as well as the static traffic conditions displayed by traffic control facilities situated on the road. Specifically, this includes road information elements such as geographical location, attributes, content, road network topology, road conditions, road alignment, and road traffic signs, traffic markings, traffic lights, and road traffic information display equipment.

When considering the comprehensive factors contributing to traffic accidents of assisted driving vehicles, the disturbance value variables of road static facility elements constitute a significant component. These disturbance value variables primarily consist of those related to road attribute elements, road facility elements, high-precision map lane information elements, traffic signal control facilities, traffic information release facilities, and traffic warning facilities.

The disturbance value variables associated with road attribute elements mainly focus on road geometry data and road association relationship data. The disturbance value of road geometry data typically pertains to the change state of the marking shape of the rightmost lane along the first lane on the left side of the passing direction of the assisted driving vehicle. This generally encompasses the change state of absolute accuracy and relative error of the plane position of lane edge lines, lane dividing lines, and special vehicle lane lines. The interference value of road association data generally encompasses the association relationship data between road entrances and roads, as well as the association relationship data between roads and navigation maps. This primarily includes information such as the change status of relevant data regarding road direction, road type, lane number, lane code, ramp type, function level, and other attributes, which may also include access control, speed limit, traffic limit, and other related data based on actual conditions.

The disturbance value variable of road infrastructure elements generally refers to the changing state of basic traffic facility components specified in traffic infrastructure construction specifications, such as traffic signs, traffic guardrails, and other components. This primarily includes the changing state of traffic signs, traffic markers, guardrails, speed bumps, various roadside facilities, bridges, tunnels, toll stations, service areas, roadside buildings, and other related road facility data.

The interference value variables of lane information elements in high-precision maps primarily consist of those related to lane data, lane attribute data, and digital traffic sign marking data. The interference value variables of lane data include the change state of lane type, lane status, lane direction, lane number, and lane restriction data, as well as related data such as lane color, lane material, lane width, and lane number. Digital traffic sign marking data interference value variables encompass geographical location, scope of application (road range, driving direction, lane, model), effective time, traffic sign marking information (category, content information, additional instructions), calibration information (digital traffic sign marking code verification), and other changes.

Disturbance value variables of traffic signal control facility elements mainly encompass interference value variables of intersection signal light elements, lane traffic signal light elements, and signal light elements at special point segments, depending on different settings. Among them, interference value variables of intersection signal light elements generally refer to the change status of signal lights at intersections of ordinary highways and urban roads, including red, green, yellow flashing warning lights, and U-turn signal changes. The interference value variable of lane traffic signal elements typically pertains to the change state of lane traffic signal lights at the entrance of highway toll stations, highway lanes, tunnels, bridges, and other sections, including red and green signal changes. The interference value variable of signal light elements in special point segments generally refers to the change state of signal lights at ramp entrances, tunnel entrances, long downhill escape lane entrances, and other special point segments, including red, green, and yellow flashing warning light changes.

The disturbance value variables of traffic information release facilities primarily include those of variable information indicator boards, variable speed limit sign screens, and service area induction facilities.

The disturbance value variables of traffic warning equipment elements mainly consist of those related to yellow flashing warning light elements, inclement weather warning device elements, temporary safety warning light elements, and traffic warning pile elements.

$$f\left(R{Q}_i\right)=\sum _{i=1}^k[f\left(S{X}_i\right)+f\left(S{S}_i\right)+f\left(G{T}_i\right)+f\left(X{H}_i\right)+f\left(F{B}_i\right)+f\left(J{S}_i\right)]$$

Let $f\left(R{Q}_i\right)$ be a function of road static facilities interfere variable for causes of traffic accident i, $f\left(S{X}_i\right)$ be a function of traffic warning equipment elements variable for $S{X}_i$, $f\left(S{S}_i\right)$ be a function of traffic warning equipment elements variable for $S{S}_i$, $f\left(G{T}_i\right)$ be a function of traffic warning equipment elements variable for $G{T}_i$, $f\left(X{H}_i\right)$ be a function of traffic warning equipment elements variable for $X{H}_i$, $f\left(F{B}_i\right)$ be a function of traffic warning equipment elements variable for $F{B}_i$, $f\left(J{S}_i\right)$ be a function of traffic warning equipment elements variable for $J{S}_i$.

3.2. Traffic Dynamic Characteristics Investigation Content

The investigation of traffic dynamic characteristics typically encompasses the assessment of information and infrastructural status concerning other vehicles, non-motor vehicles, pedestrians, and other traffic participants. This mainly involves micro-level analysis of traffic participant types, location trajectories, travel purposes, speeds, as well as macro-level examination of road network traffic flow states, traffic events, traffic organization, and other dynamic traffic information.

In the context of comprehensive factors leading to traffic accidents involving assisted driving vehicles, the disturbance value variable of traffic dynamic feature factors plays a pivotal role. This disturbance primarily comprises variables related to traffic flow factors, traffic event factors, and infrastructure state factors.

The disturbance value variables associated with traffic flow factors encompass those pertaining to motor vehicle driving speeds, non-motor vehicle driving speeds, motor vehicle traffic volumes, non-motor vehicle traffic volumes, motor vehicle traffic densities, and non-motor vehicle traffic densities.

Regarding traffic event factors, the disturbance value variables include those related to the location and occupation time of lanes by external traffic accidents, traffic control measures, traffic congestion scenarios, congestion durations, vehicle retrograde instances, pedestrian intrusions, spilling incidents, and bridge or tunnel fire events, among other primary interference variables.

Moreover, the disturbance value variables of essential facility condition monitoring elements mainly consist of those associated with pavement condition monitoring, slope condition monitoring, bridge structure condition monitoring, and tunnel structure condition monitoring.

$$f\left(D{T}_i\right)=\sum _{i=1}^k[f\left(J{T}_i\right)+f\left(S{T}_i\right)+f\left(Z{T}_i\right)]$$

Let $f\left(D{T}_i\right)$ be a function of Traffic dynamic characteristics variable for causes of traffic accident i, $f\left(J{T}_i\right)$ be a function of traffic warning equipment elements variable for $J{T}_i$, $f\left(S{T}_i\right)$ be a function of traffic warning equipment elements variable for $S{T}_i$, $f\left(Z{T}_i\right)$ be a function of traffic warning equipment elements variable for $Z{T}_i$.

3.3. Investigation of Transient Weather Conditions

The investigation of instantaneous weather conditions primarily involves examining traffic meteorological environment monitoring information, which focuses on the impact of meteorological changes on the safety of assisted driving vehicles. Key factors under consideration include visibility, road temperature, road surface conditions, wind speed, wind direction, precipitation, and freezing degree.

The disturbance value variables associated with instantaneous weather conditions mainly consist of visibility disturbance variables, road temperature disturbance variables, road surface condition disturbance variables, wind speed disturbance variables, wind direction disturbance variables, and precipitation disturbance variables.

$$f\left(T{Q}_i\right)=\sum _{i=1}^k[f\left(E{V}_i\right)+f\left(E{T}_i\right)+f\left(E{R}_i\right)+f\left(E{V}_i\right)+f\left(E{R}_i\right)+f\left(E{W}_i\right)+f\left(E{D}_i\right)+f\left(R{A}_i\right)]$$

Let $f\left(T{Q}_i\right)$ be a function of road static facilities interfere variable for causes of traffic accident i, $f\left(E{V}_i\right)$ be a function of traffic warning equipment elements variable for $E{V}_i$, $f\left(E{T}_i\right)$ be a function of traffic warning equipment elements variable for $E{T}_i$, $f\left(E{R}_i\right)$ be a function of traffic warning equipment elements variable for $E{R}_i$, $f\left(E{V}_i\right)$ be a function of traffic warning equipment elements variable for $E{V}_i$, $f\left(E{R}_i\right)$ be a function of traffic warning equipment elements variable for $E{R}_i$, $f\left(E{W}_i\right)$ be a function of traffic warning equipment elements variable for $E{W}_i$, $f\left(E{D}_i\right)$ be a function of traffic warning equipment elements variable for $E{D}_i$, $f\left(R{A}_i\right)$ be a function of traffic warning equipment elements variable for $E{W}_i$.

3.4. Road Environment Interference Model

The road environment factor interference model of assisted driving vehicle traffic accident was constructed.

$$f\left(H{J}_i\right)=\sum _{i=1}^k[{\mu }_{j}f\left(R{Q}_i\right)+{\psi }_{j}f\left(D{T}_i\right)+{\omega }_{j}f\left(T{Q}_i\right)]$$

Let $i\in M,{\mu }_j+{\psi }_j+{\omega }_j=1$. Let $f\left(H{J}_i\right)$ be a function of Road environment interference model, j is the weight coefficient of causes i on traffic accidents of assisted driving vehicles, ${\mu }_j$ is the weight coefficient of road static facility elements, ${\psi }_j$ is the weight coefficient of traffic dynamic feature elements, ${\omega }_j$ is the weight coefficient of weather instantaneous conditions, $f\left(R{Q}_i\right)$ be a function of road static facilities interfere variable, $f\left(D{T}_i\right)$ be a function of Traffic dynamic characteristics variable, $f\left(T{Q}_i\right)$ be a function of road static facilities interfere variable.

4. Accimap Causation Analysis

In this section, the Accident Map (AcciMap) method is employed to analyze each accident, incorporating the accident causation analysis results derived from the road environment interference layer within the STPA (System-Theoretic Process Analysis) framework. A typical traffic accident involving an assisted driving vehicle is selected and illustrated in Figure 2. At the time of the accident, the weather was clear with excellent visibility. However, the driver of the assisted driving vehicle had neglected driving for an extended period, causing the vehicle to disengage from the navigation-assisted driving mode and automatically downgrade to adaptive cruise mode. Consequently, the assisted driving vehicle lost its guiding function as it approached a section with an excessively large abduction slope at the guardrail’s end and an unusually long distance between guardrail columns, leading to a collision with the side-outstretched end guardrail. An in-depth investigation and analysis of this traffic accident revealed that the safety awareness of human drivers of assisted driving vehicles (hereinafter referred to as human drivers), road markings, and the settings of roadside guardrails all have indirect impacts on the occurrence of such accidents.

All accident participants in the accident elements are summarized, and there are 25 categories of participants, as shown in Figure 3 Among all the participants, the level of interference of road environment elements (40 percent) is more, followed by enterprise management (32 percent), department supervision (8 percent), assisted driving vehicle technology (8 percent), accident process and driver operation (8 percent), and the level of government decision-making (4 percent).

In the Actormap analysis, as shown in Figure 3, participants of the road environment factor interference layer and enterprise management (32 percent) accounted for 72 percent, indicating that in the traffic accidents of assisted driving vehicles, road environment interference and enterprise management factors contributed more to the accident. The participants at all levels in the ActorMap table are shown in

Table 1. AcciMap analysis of accident.

Hierarchy	Participant
National committees	Government
Regulatory bodies and associations	Emergency rescue department, Transportation department
Enterprise management	Automobile sales enterprises, Automobile production enterprises, Road maintenance units, Engineering consulting and supervision units, Construction and construction units, Heavy-duty semi-trailer enterprises, Online car hailing enterprises, Hitch platforms
Technical and operational management	Human driver, Auxiliary driving vehicle
Driving processes	Human driver, Auxiliary driving vehicle
Road environmental elements interference	Central dividing belt guardrail, Construction area warning signs, Construction area anti-Collision vehicles, Construction area warning signs, Construction area protection facilities, Side guardrail end, Side guardrail column, Road marking, Safety island end anti-collision drum, Heavy semi-trailer driver

. Among them, the management level of the enterprise more reflects the road maintenance units, engineering consulting and supervision units, and construction units. For example, the road maintenance units have not effectively implemented the main responsibility of the central guardrail hidden danger management, and the construction units have insufficient safety facilities and protection capabilities for the traffic organization on the construction site. The interference layer of road environmental factors reflects the influence of central divider guardrail, side guardrail and road marking on the traffic accident process of assisted driving vehicles.

Using AcciMap method to analyze the causes of typical assisted driving vehicle accidents from 6 levels, the causes of each accident are summarized. In the AcciMap analysis of 5 accidents, a total of 104 accident causes were identified, among which 46 causes were separate and 58 factors were similar to other causes. After classifying and combining the same accident causes, a total of 59 accident factors were obtained, as shown in Figure 4, which described the accident causes corresponding to each accident participant. N indicates the number of times the cause occurs. As shown in Figure 5, the size of the circle and the thickness of the connecting line in the figure respectively represent the number of occurrence of the accident cause and the number of the accident cause connection. The AcciMap map corresponds to the number in the accident element map. In AcciMap, the direct causes of accidents can be summarized as human drivers’ over-reliance on the auxiliary driving function and the application limitations of the auxiliary driving function, which are specifically manifested as human drivers speeding and not paying attention to driving for a long time. The auxiliary driving vehicle failed to identify the heavy special operation vehicle in front of it, failed to identify the support pier located in the center of the lane, failed to identify the lane, failed to identify obstacles such as piles and drums. The causal relationship between accident causes and accident causes at different levels in the AcciMap analysis will be explained next.

“National committees” encompass four key elements, with four accidents specifically pertaining to the investigation, management, and supervision of road hazards. These accidents are primarily attributed to non-compliance issues concerning road central separation guardrails, roadside guardrails, and construction site safety protections. Additionally, two accidents involved industry management related to the production and sales of assisted driving vehicles. These incidents were influenced by “enterprise management” car sales enterprises’ exaggerated promotion of assisted driving functionalities, resulting in human drivers’ lack of in-depth understanding and excessive reliance on these functions. One accident was indirectly influenced by the local government’s lack of regulations concerning hitch vehicles.

“Regulatory bodies and associations” consist of three elements, one of which is influenced by the “government decision-making layer”. This led to inadequate supervision of network cars and insufficient investigation and management of hidden hazards. Furthermore, two accidents affected road maintenance units that failed to effectively fulfill their primary responsibility in managing central care hidden hazards.

“Enterprise management” comprises 14 elements, primarily describing the impact of management behaviors from assisted driving vehicle production enterprises, sales enterprises, road maintenance units, engineering consulting and supervision units, construction units, and other institutions on other levels. Elements 10 and 11 outline reasons pertaining to road maintenance units, while six elements detail reasons concerning construction units in guardrail design, construction, acceptance, rectification, and other aspects. Three factors highlight automobile production and sales enterprises’ insufficient safety tips for drivers and exaggerated promotion of assisted driving functions, contributing to accident occurrences.

“Technical and operational management” includes six elements, with four accidents involving human drivers’ over-reliance on assisted driving functions and three accidents related to the application limitations of assisted driving vehicles’ adaptive cruise function. Elements 24, 25, 26, and 27 pertain to vehicle functional accessories such as assisted driving vehicle batteries, doors, identification modules, and adaptive cruise function modules.

“Driving processes” consist of 22 elements, primarily focusing on the human driver, the behavior of the assisted driving car, and the accident outcome. This level is closely associated with the “assisted driving vehicle technology level”. Among these, speeding is the most frequent occurrence, closely tied to human drivers’ failure to maintain timely vehicle control and take emergency measures. Elements 34, 36, 37, 38, and 39 respectively describe accident patterns such as collisions between assisted driving vehicles and toll station safety islands, collisions with high-speed railway bridge support piers, collisions with heavy special operation vehicles, frontal collisions with heavy semi-trailers, and collisions with the outer ends of roadside guardrails.

“Road environmental elements interference” encompasses 10 elements. Among these, elements 51, 52, 53, and 54 are related to construction area safety protections, while elements 50, 55, 56, 57, and 58 are associated with road infrastructure. Among the road environmental elements, defects in traffic signs, the absence of safety signs in construction areas, and unreasonable guardrail settings impact assisted driving vehicles’ decision-making and human drivers’ operational behavior. By combining the interference model of road environment factors in assisted driving vehicle traffic accidents, the road environment interference factors are classified, as illustrated in

Table 2. Statistical table of road and environmental factors.

Element Classification	Specific Content
Road static facility elements	The central divider guardrail is not set up as required
	Insufficient warning signs in construction area
	Insufficient protection facilities in construction area
	The abduction slope of the end of the side guardrail is too large
	There is no filler in the anti-collision drum at the safety island end
	The distance between the guardrail columns on the side of the road is too long
	The transition defect of the connection between the graded section and the straight section of the road marking in the square of the road marking toll station
Traffic dynamic characteristic elements	Anti-collision cushion of heavy special operation vehicle in construction area is not completely put down
	The driver of the heavy semi-trailer did not observe the operation specifications when overtaking
Weather transient condition elements	Inapplicability

.

5. Discussion and Conclusions

Compared to traditional methodologies for analyzing road traffic accident causation involving conventional and autonomous vehicles, the investigation scope of automobile accidents featuring assisted driving capabilities extends to encompass the operational behaviors of both the assisted driving system and the human drivers within the vehicles. The interplay between road environment interference factors and these entities is significantly more intricate. This functionality necessitates distinct requirements for perceiving road environment elements. Consequently, this paper endeavors to augment the investigation framework of road environment interference factors pertinent to traffic accidents involving assisted driving vehicles in human-machine collaborative scenarios. This augmentation occurs once the essential components required by a specific driving assistance function are established, building upon the foundation of vehicle and driver investigation contents. The factors comprising the road environment interference layer are meticulously analyzed, offering content and methodological guidance for accident investigation and cause analysis in automobile accidents involving assisted driving functions. The key findings are outlined as follows:An interference model for road environment factors in traffic accidents of assisted driving vehicles is formulated. Leveraging STPA (System-Theoretic Process Analysis) theory, this paper dissects the structure of the vehicle control layer, vehicle driving execution layer, and control feedback layer during assisted driving vehicle accidents. By integrating factors such as road static facilities, traffic dynamic characteristics, and instantaneous weather conditions, an interference model tailored to road environment factors in assisted driving vehicle accidents is constructed. Furthermore, the investigation scope and data points concerning road environment factors related to these accidents are clearly defined. This paper introduces a method based on the integration of STPA and AcciMap to delve into the road environment causes of traffic accidents involving assisted driving vehicles. By synthesizing the results from STPA analysis on road environment interference layer accident causes, the AcciMap methodology is employed to conduct an in-depth exploration of typical assisted driving vehicle accident cases. All accident participants within the traffic accident factors are categorized and analyzed across six hierarchical levels, leading to the refinement of 59 accident factors. These factors collectively contribute to the formation of traffic accident causes in assisted driving vehicles, with the results summarized and analyzed comprehensively. The interconnections among the factors causing traffic accidents in assisted driving vehicles are elucidated. Through correlation analysis of factors at each hierarchical level, the logical relationships and influence mechanisms both within and between levels are clarified. A preliminary qualitative and quantitative analysis approach for assessing the interference degree of road environment factors in assisted driving vehicle accidents is also explored. The research findings provide investigative methodologies and insights into road environment interference factors for vehicles equipped with auxiliary driving functions below Level L3, as well as autonomous vehicles with partially enabled auxiliary driving functions at Level L3 and above. Building upon the investigation framework of vehicles and drivers, this study appends the investigation contents and methods pertinent to road environment interference factors in assisted driving vehicle accidents within human-machine collaborative scenarios. This contribution offers a foundation for future research on preventive countermeasures against traffic accidents involving assisted driving vehicles.The primary limitations of this study encompass several areas. In terms of data collection, the restricted sample size of traffic accidents involving assisted driving vehicles could potentially lead to analysis results that lack adequate representativeness. Furthermore, the reliance solely on accident reports and in-vehicle electronic data presents a challenge, as the absence of long-term tracking data from video footage and other real-road environments may hinder a comprehensive understanding of actual driving behaviors and environmental interference factors. With respect to the intricacies of environmental interference, the complex interplay between road environmental factors (such as weather and lighting conditions) and driver behavior, as well as vehicle performance, poses a significant hurdle. Existing models may find it difficult to fully encapsulate these nonlinear relationships.