### Related Works  
The existing traffic laws compliance motion planning approaches can be broadly divided into AI-based, rule-based, and optimization-based approaches. Most AI-based approaches represent traffic laws as constraints or reward functions. Constrained Markov decision processes are employed to train decision strategies, including methods based on Lagrange multipliers [10; 11] and constraint strategy optimization [12]. These methods can effectively handle straightforward traffic laws. Moreover, considering penalties for violating traffic laws in the reward function is also a common approach [13]. However, due to the black-box nature inherent in AI-based approaches, their ability to meet the stringent reliability requirements of autonomous driving systems necessitates further research. Compared to AI-based approaches, rule-based approaches offer superior controllability and interpretability. Rule-based approaches incorporate traffic laws into motion planning and have already found several successful applications. These predominantly encompass discrete input space planning based on collision detection [14], finite state machine [15], constraint optimization [16], and reachable sets [17]. [PERSON] et al. [18] utilized static traffic laws as the foundation for generating a set of candidate behaviors. It comprehensively considers factors such as efficiency and safety, allowing vehicles to determine the optimal decision while complying with traffic laws and having high consistency with manual decision results. [PERSON] et al. [19] combined rule-based approaches with optimization-based approaches, formulating strategies for compliance state transitions. The constraints and references in motion planning are modified based on predictions of non-compliant behaviors, enabling AVs to obey traffic laws on the highway. However, most rule-based approaches need typical hand-crafted rule design, which adds a degree of difficulty to devising decision-making solutions in complex scenarios, especially for some unforeseen scenarios. Optimization-based approaches integrate multiple optimization objectives into a cost function, adjusting weights accordingly, thus prioritizing diverse driving goals [20; 21]. [PERSON] et al. incorporate traffic laws into the cost function, while obstacle avoidance serves as the constraint in motion planning [22; 23]. [PERSON] et al. [24], [PERSON] et al. [25], and [PERSON] et al. [26] utilized the artificial potential field (APF) to represent traffic laws and integrate them into the cost function. This approach allows for the successful execution of the driving task while considering various factors. These methods consider traffic laws as high-cost factors, particularly in the context of prohibited regulations, such as solid line and stop line adherence. [PERSON] et al. [27] utilized game theory to optimize multi-objective interactive behaviors, considering collision and violation in a reward function. [PERSON] et al. [28] proposed a minimum violation planning framework that assigns higher weights to collision avoidance and lower weights to traffic law compliance in the cost function. Thispermits AVs to breach traffic laws to ensure safety. However, assigning lower weights to traffic law compliance potentially leads to violations, even in non-conflict scenarios. For example, the vehicle could have evaded the collision by abruptly braking within the lane but instead opted for a trajectory that transgressed the solid line. Table 1 lists recent trajectory planning research, offering an analysis of the factors taken into account by each method. To summarize, most of the research regards traffic laws as strong constraints or high-cost factors and does not account for behaviors that extend beyond traffic rules, thereby reducing the effectiveness of decision-making in conflict scenarios.