Advancements in collision avoidance techniques for internet-connected vehicles: A comprehensive review of methods and challenges

Collision avoidance (CA) in internet-connected vehicles (ICVs) is critical for ensuring safety and efficiency in smart transportation systems. The ICV control system achieves CA through integrated features, including sensor-based perception, communication technologies, and data-driven artificial intelligence, enabling real-time optimization for smooth cruising. By using these adaptive architectures on one platform, ICVs enhances both individual-level vehicle performance and network-wide traffic efficiency. This review examines a wide range of CA methods and strategies, offering comprehensive insights into how ICV systems detect and avoid obstacles in dynamic environments. We critically assess existing research, evaluating the effectiveness, challenges, and future directions of CA techniques, with particular attention to static and dynamic obstacle handling and interactions with other road users. Furthermore, we systematically explore ICV control systems, emphasizing how integrated technologies improve safe mobility and accident prevention. Our research synthesizes findings from peer-reviewed journals and conference proceedings (primarily from the past decade) to support the development of robust CAsystems. These insights aim to advance reliable CA frameworks for ICVs, fostering safer and more efficient transportation networks.