ADAS Alarm Sound Design for Autonomous Vehicles Based on Local Optimization: A Case Study in Shanghai, China

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 Shanghainese 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.

Keywords

• advanced driver-assistance system
• human–machine automation
• auditory warnings
• autonomous vehicles