Real-Time AI-Enhanced Systems for Autonomous Vehicle Navigation in Adverse Weather Conditions

Abstract

From communicating with spacecraft billions of miles away to recognizing spoken language for people with disabilities, artificial intelligence (AI) has become a keystone in complex systems in recent years. Fully autonomous and AI-enhanced self-driving car navigation have received growing attention. In adverse weather conditions or high-temperature cities, one of the biggest challenges that autonomous vehicles encounter is the "Rain-Blindness Effect," where the perception system may fail to function normally due to torrential rain or dust storms.

Real-time navigation systems play a crucial role in many practical applications, such as autonomous vehicles and networked pedestrian navigation. A real-time system can accomplish rapid responses to sudden emergencies, such as traffic jams or severe climate conditions. Considering the striking advantages of real-time navigation systems, our work is dedicated to integrating robust AI enhancement algorithms to facilitate real-time performance. These algorithms allow the system to adapt to different scenarios and boost the competence of the navigation platform. However, in recent years, end-to-end models and reinforcement learning have become increasingly popular, and we believe that a pivot in the current trends back to the full real-time paradigm would have far-reaching consequences. Despite the evident high computational and data complexity, a real-time AI-enhanced navigation system would complement the holistic view presented in this essay, demonstrating strong potential to become a successful competent model in real-time navigation scenarios. For machines to operate in the real world, the success of real-time navigation tasks is more sensitive to the performance of the system. Furthermore, the growing trends toward assisted autonomous driving have also made the fast and robust completion of decision makers and controllers important.