## 1 Introduction  
Research in the field of automotive applications plays a significant role in advanced automotive technology, improving automotive performance, safety, and enhancing the user experience. It also facilitates the application of intelligence, connectivity, and automation in the automotive industry, making it of vital importance. Common research areas in automotive applications include automotive communication network security [1], interactive autonomous driving automotives [2], and the design of functional application models for in-automotive information and entertainment systems [3]. Establishing interactive tools between automotive users and in-automotive systems is a crucial aspect of achieving intelligence in the automotive industry. In the context of intelligent automotive applications, NLU plays a critical role in the automotive environment. NLU refers to the ability of a computer system to comprehend and process natural language expressions. Itinvolves techniques such as natural language processing and text classification, aiming to divide the text into different categories or labels. By classifying the intent of user-spoken commands, automotive applications can gain a more accurate understanding of user intentions and requirements, enabling them to provide intelligent and personalized responses and services. In the multi-intent scenarios of NLU, users may convey multiple related intents simultaneously through a single sentence or a conversation. Given the semantic similarity and overlap among intents, the precise identification and classification of distinct intents within automotive user commands pose significant challenges. Furthermore, the scarcity of data can result in diminished performance of multi-intent detection models. Meanwhile, the endeavor of creating an expansive corpus that encompasses a variety of intents expressed in spoken language adds another layer of complexity to the task. The primary objective of this paper's research is to address the issue of multi-intent detection within the context of automotive user commands. By focusing on this matter, the study aims to elevate the precision and effectiveness of NLU systems in intricate dialogues specific to the automotive application domain, thereby offering substantial assistance in enhancing interactions and performance within automotive applications. Traditional multi-intent joint detection methods [4; 5] often employ fixed weights to assign importance to each intent, which may not meet the demands as more data and tasks are incorporated. Further research has unveiled a robust connection between intent detection and slot filling, and mainstream models [6; 7; 8] consider establishing the interconnection between intents and slots, exploring the utilization of joint learning techniques and deep learning algorithms to allocate weights adaptively. By leveraging the interaction between intent detection and slot filling, they aim to enhance the accuracy of intent detection, with a primary focus on single-intent detection tasks. Regarding multi-intent detection tasks, the joint multiple ID-SF model [4] explores a multi-tasking framework with a slot-gating mechanism for combined multiple-intent detection and slot filling. However, this approach fails to furnish detailed intent information to guide token-level slot filling. In the AGIF model [5], an intent-slot graph interaction layer is incorporated to capture the significant correlation between slots and intents. Furthermore, [PERSON] et al. [6] propose a joint multi-intent detection and slot-filling model based on BERT, explicitly mapping slots to intents. However, this model lacks bidirectional slot-intent constraints and underutilizes detailed information in the slot-filling task. Presently, deep learning algorithms stand as the prevailing method for tackling the intricacies of multi-intent detection. Nevertheless, attaining a high level of precision using these algorithms mandates a substantial reservoir of training data, particularly within widely spoken languages like English.