A Review of LiDAR-Based 3D Object Detection via Deep Learning Approaches Towards Robust Connected and Autonomous Vehicles

Automated Driving Systems (ADS) rely on a variety of sensors and algorithms to perceive their environment and make proper and timely driving decisions to ensure safety and efficiency. One of their critical tasks is object detection, which detects and classifies object presence with a specific emphasis on cars, pedestrians, and cyclists, in 3D space in their surroundings with the use of sensing data from those sensors, including LiDAR. 3D object detection in Autonomous Vehicles (AVs) is a challenging problem due to the complexity and variability of real-world scenarios, such as occlusions, varying lighting conditions, and diverse object shapes and sizes. Moreover, the limitations of computing power and the availability of information about the surrounding environment, combined with the quality and age of sensors installed in standalone AVs, often lead to poor perception performance. In this context, Connected and Autonomous Vehicles (CAVs) have become a promising solution that can be employed to harness connectivity and external information and enhance their perception capabilities. In this paper, we provide a review of existing 3D object detection techniques based on deep learning for ADS using data from LiDAR and other sensors, as well as the architecture of CAVs and their communications, which facilitate heterogeneous networks of mobile and satellite communication technologies. We also discuss real-world and synthetic datasets for both single-vehicle and multi-view vehicles, such as vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X). Finally, we review the applications and future outlook of 3D object detection.