A Survey of Driving Safety With Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance

TLDR

Accurately detecting hazards and issuing timely warnings or autonomous control is central to collision‑avoidance systems, and advances in sensing, vehicular communications, and AI promise greater environmental awareness and decision speed. This survey reviews state‑of‑the‑art data‑driven collision‑avoidance techniques that integrate sensing, vehicular communications, and AI. The authors systematically examine the CA pipeline, evaluate enabling sensing and communication methods, compare AI algorithms, and summarize testbeds and future research challenges.

Abstract

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.