Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey

TL;DR

This survey reviews infrastructure-based object detection and tracking systems for cooperative driving automation, highlighting architectures, sensors, methodologies, datasets, and future research opportunities to enhance vehicle perception beyond onboard limitations.

Contribution

It provides a comprehensive overview of infrastructure-based perception systems, analyzing architectures, sensors, methods, datasets, and future trends in cooperative driving automation.

Findings

Infrastructure sensors enhance perception beyond onboard limitations.

Various perception architectures and methodologies are analyzed.

Open problems and future research directions are discussed.

Abstract

Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies could provide satisfactory object detection results in occlusion-free scenarios, the perception performance of onboard sensors could be inevitably limited by the range and occlusion. Owing to flexible position and pose for sensor installation, infrastructure-based detection and tracking systems can enhance the perception capability for connected vehicles and thus quickly become one of the most popular research topics. In this paper, we review the research progress for infrastructure-based object detection and tracking systems. Architectures of roadside perception systems based on different types of sensors are reviewed to show a high-level description of the workflows for infrastructure-based perception systems. Roadside sensors and different perception methodologies are reviewed and analyzed with detailed literature to provide a low-level explanation for specific methods followed by Datasets and Simulators to draw an overall landscape of infrastructure-based object detection and tracking methods. Discussions are conducted to point out current opportunities, open problems, and anticipated future trends.