Unifying ground and air: a comprehensive review of deep learning-enabled CAVs and UAVs

Abstract

The tremendous advancements in artificial intelligence (AI) techniques, particularly those pertinent to computer vision and image recognition, are revolutionizing the automotive industry towards the development of intelligent transportation systems for smart cities. Integrating AI techniques into connected autonomous vehicles (CAVs) and unmanned aerial vehicles (UAVs) and their data fusion, enables a new paradigm that allows for unparalleled real-time awareness of the surrounding environment. The potential of emerging wireless technologies can be fully exploited by establishing communication and cooperation among AI-augmented CAVs and UAVs. However, configuring appropriate deep learning (DL) models for connected vehicles is a complex task. Any errors can result in severe consequences, including loss of vehicles, infrastructure, and human lives. These systems are also susceptible to cyber attacks, necessitating a thorough and timely threat analysis and countermeasures to prevent catastrophic events. Our findings highlight the effectiveness of AI-driven data fusion in enhancing cooperative perception between CAVs and UAVs, identify security vulnerabilities in DL-based systems, and demonstrate how V2X-enabled UAVs can significantly improve situational awareness in corner cases.