# A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC
## 1 Introduction  
In recent years, autonomous vehicles (AVs) have received significant attention due to their potential to satisfy various driving demands, such as collision avoidance and passenger comfort [1; 2; 3; 4; 5; 6; 7]. When addressing the complex challenge of integrated multi-demands, the prevailing approach involves formulating it as a multi-objective optimization problem [8; 9]. In this context, the cutting-edge methodologies encompass Reinforcement Learning [10; 11; 12; 13] and NMPC [14; 15; 16]. This paper specifically focuses on NMPC-based methods due to their clarity of mechanism. Ensuring the satisfaction of diverse driving demands while maintaining computational efficiency is a paramount concern. When designing an NMPC planner under the TMO strategy, the process typically commences with the modeling of driving demands. Subsequently, fundamental demands, such as trajectory tracking, passenger comfort, and fuel economy, are aggregated in a weighted manner to construct the objective function. High-priority demands, including collision avoidance, dynamic stability, and adherence to traffic rules, are typically established as constraints [14; 15; 16; 17; 18; 19; 20; 21; 22]. For instance, references [15; 16; 17] developed an NMPC planner for emergency obstacle avoidance. They incorporated trajectory tracking errors into the objective function while imposing collision avoidance and dynamic stability models asconstraints. Reference [8] integrated tracking error and control variables into the objective function, simultaneously applying speed limits and solid lane constraints. While these methods yielded favorable results, the TMO strategy encounters a challenge as the number of considered demands escalates. This results in a proliferation of constraints, leading to an exponential increase in the optimization dimension, subsequently diminishing computational efficiency [4]. This study encompasses the integration of prevalent driving demands, which include dynamic stability, collision avoidance, adherence to traffic rules (such as red light compliance, staying within solid lane lines, and observing maximum speed limits), fuel efficiency, passenger comfort, and tracking capability. While there is a dearth of research explicitly addressing all eight of these driving demands, it is evident that the TMO strategy is intrinsically unsuitable for this problem. In certain situations, such as when an AV is faced with the loss of dynamic stability, considerations such as driving comfort or fuel economy become secondary [18; 22]. Combining the latter demands and the former incurs conflict and can potentially lead to collisions. Conversely, during simple tasks where dynamic stability is not a primary concern, AVs have no need to consider this aspect [14; 21]. In such instances, the control of the vehicle operates well within the bounds of dynamic stability, rendering the dynamic stability constraint redundant. Eliminating this redundancy reduces the optimization dimension without compromising control performance. To effectively address the issues of conflict and redundancy among driving demands (redundancy refers that the constraint corresponding to some demand is ineffective, i.e., removing the constraint does not affect the solution of optimization), this paper introduces HMD strategy to guide planner design. Initially, driving demands are systematically prioritized based on their impact on driving safety. Subsequently, leveraging these priorities, the HMD strategy assesses the potential risks of violating driving demands in a hierarchical sequence. To bolster safety, lower-priority demands that clash with higher-priority counterparts are either omitted or attenuated. Additionally, any demands that can be currently satisfied are removed. This process results in the segmentation of the demand domain into several sub-domains (see Figure 1). The motion planner focuses exclusively on the demands within one of these sub-domains. Several evaluation functions are established to gauge the risk of violating driving demands.