Research Advances and Challenges of Autonomous and Connected Ground Vehicles

TLDR

Autonomous vehicle technology promises safe and convenient transportation, but real‑world complexity hampers reliable operation, and vehicle connectivity in CAVs improves situational awareness and cooperation, enhancing robustness and making CAVs a promising future solution. This paper introduces a representative CAV architecture and surveys recent advances, methods, and algorithms for sensing, perception, planning, and control. It reviews a multi‑layer Perception‑Planning‑Control architecture, covering on‑board sensors, vehicular communications, sensor fusion, localization, mapping, decision making, trajectory planning, and control strategies, while summarizing connectivity implementations and the challenges of cooperative perception, complex decision making, and multi‑vehicle control. The review lists remaining challenges and unsolved problems in each section, providing a comprehensive resource useful to researchers, practitioners, and students.

Abstract

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.