###### Abstract  
Alarm sounds significantly influence a user's sensory perception while driving, directly affecting driving judgement and safety. Personal experience and the environment play an important role in information cognition, but they are rarely considered in the current warning design. We propose a methodology enabling engineers and designers to locally optimize the advanced driver-assistance system (ADAS) functions and applied it to the Shanghaiese ecosystem to improve performance. The alarm sound content is studied and sorted out to conduct user research and spatial sound collection evaluation. Local optimization and the subdivision of data are carried out to generate a user perception set on which the experimental tests and evaluation analysis are implemented. The framework increases the overall efficiency of auditory warning systems and minimizes Human-Machine Interface misunderstandings, thus providing the optimal security scheme for users. advanced driver-assistance system; human-machine automation; auditory warnings; autonomous vehicles +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-00000-00000-00000-0000-00000  
levels [11; 14]. Many studies later showed that hybrid interfaces (using both acoustic and visual information) could be even more efficient than sound-alone and visual-alone ones [15]. Recent research has even tried to exploit sound to the next level. Several possibilities have been explored. One idea is to use more realistic sounds (\"earscapes\") when compared to abstract ones (\"earcons\") to convey an idea in a faster and easier-to-learn manner [16]. Another idea is to explore spatialized sounds. With these sound systems, not only is sound conveying natural information, but its localization can also be interpreted by the driver [17]. Although efficient, these systems are not always easy to engage. Over the years, many studies have been conducted to search for the best-suited sounds to use in ADASs [15; 16; 18]. Advancements in engineering have improved sound delivery, such as digital sound production. By creating specific sound-descriptive arrays (e.g., timbre, loudness, and roughness array), engineers and researchers were able to create a wide panel of sounds. Most interface engineers and car manufacturers still use short, abstract sounds rather than speech or realistic sounds in the Human-Machine Interface (HMI). Research has indeed shown such abstract tones to be much more efficient than visual cues [10] and to allow better response times [14]. However, research has also highlighted that abstract sounds are not always the best when it comes to intelligibility [11]. Abstract sounds need learning from the operator. Moreover, an operator can only assimilate four to six sounds quickly [18]. Therefore, using realistic-based sounds or metaphorical ones has proven efficient in research, although this approach has not always been put into practice by car manufacturers. Yet, the design of the HMI cannot only revolve around the choice of sound characteristics. Many other challenges must be taken into account. One of these concerns is workload and annoyance. The workload is the main factor to address regarding the driver. If it is too high, a loss of performance will happen. If it is too low, the operator will surely engage more frequently in secondary tasks. The secondary tasks may decrease the driver's situational awareness [19]. Such a decrease concurrently dramatically increases accident risks. Correct balance must be found in the HMI so that sound may help keep drivers' focus on the road, while also enabling them to engage safely in secondary tasks. [PERSON] et al. [17] found that speech alerts improved memory for events in automatic driving, and the auditory display resulted in less perceived effort in the study tasks but increased annoyance. Some studies have studied the design of auditory feedback in IVIS human-computer interactions with multimodal inputs and outputs.