## 1 Introduction
As a result, the problem of environmental risk assessment and local path planning for smart vehicles is receiving increasing attention from the scientific community. The primary modeling entities for the current major research mostly pertain to cars because they are the primary traffic players in the vehicle driving scenario. Existing local path planning methodologies can be divided into three primary groups: those that use driver intent prediction, driver physics models, and social interaction. For example, ref. [6] constructs costly maps and obtains the least costly local path by the RRT (Rapidly-exploring Random Trees) method. Whereas [7] reduces the risk during driving by computing trajectories that maximize visibility, the problem of violating certain rules often arises. To avoid such problems, ref. [8] proposed a method to synthesize only the motions that violate the lowest priority rules in the shortest time possible, and although it seems to work, the integrated challenge of autonomous driving makes it challenging in non-deterministic systems and environments. In addition, In [9], the authors use two optimal search algorithms, A* and D*, to find the globally optimal path that connects the initial position of each cell to the target position. The researchers in [10] used the tentacle method to generate a set of tentacles as feasible trajectories on a self-centred occupancy grid around a running vehicle. Among these, the artificial potential field-based approach, in the area of autonomous cars, includes risk assessment techniques based on vehicle physics models and driver intent-based predictions, enabling efficient risk modeling of the immediate environment surrounding the vehicle. Due to its ability to easily visualize the static environment information surrounding a robot, the traditional artificial potential field method has historically been one of the most well-liked research directions for robot path planning algorithms. However, while the algorithm has a workable solution in time, it has the disadvantage of exponentially increasing in spatial complexity as the spatial extent increases. Additionally, it is not guaranteed to produce globally optimal outcomes due to its weak capacity to service local minimum traps. To address this issue, numerous researchers have developed techniques including shape representation approaches [11] and simulated annealing algorithms [12]. Ref. [13] constructed a discrete artificial potential field, which was customised and tuned to achieve a fast and efficient solver that could operate in near real-time in complex environments. A unified model of \"driving risk field\" was constructed for the closed-loop system of human-vehicle-road in order to expose the mechanism of human-vehicle-road interaction, the influence law of each element of traffic on driving safety, and to anticipate the dynamic changing trend of driving risk [14]. The model is based on field theory and introduces the new concept of \"driving risk field\", which includes \"kinetic field\" determined by motor vehicles, non-motorized vehicles, and other moving objects on the road, \"potential field\" produced by road environment elements, and \"behavior field\" determined by individual characteristics of drivers. Based on the findings of the research, ref. [15] proposed a spring-like model risk assessment method to improve the artificial potential field. When combined with the minimum action method, the action generation problem was subsequently reduced to an optimization problem, leading to improved experimental outcomes at the intersection. However, because the spring-like model created by the model is unable to properly separate some unimportant aspects, it is challenging to guarantee the smooth passage of vehicles in complicated road conditions. This work provides a unified driving operation risk field model and enhances the artificial potential field creation method to circumvent such issues.