IMaR Research Centre, Munster Technological University, V92 CX88 Tralee, Ireland.
School of Science Technology, Engineering and Mathematics, Munster Technological University, V92 CX88 Tralee, Ireland.
Sensors (Basel). 2021 Mar 18;21(6):2140. doi: 10.3390/s21062140.
With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.
随着传感器和通信技术的重大进步，以及障碍物检测技术和算法的可靠应用，自动驾驶正成为一项颠覆性技术，可以改变交通和出行的未来。传感器是自动驾驶系统中感知车辆周围环境的基础，多种集成传感器的使用和性能可以直接决定自动驾驶车辆的安全性和可行性。传感器校准是任何自动驾驶系统及其组成传感器的基础，必须在进行传感器融合和障碍物检测过程之前正确执行。本文评估了自动驾驶汽车中常用传感器的性能和技术性能，主要关注大量的视觉摄像机、激光雷达传感器和雷达传感器，以及这些传感器在实际应用中可能遇到的各种情况。我们概述了三种主要的传感器校准类别，并回顾了现有的多传感器校准开源校准包及其与众多商业传感器的兼容性。我们还总结了三种主要的传感器融合方法，并回顾了当前用于自动驾驶应用中目标检测的多传感器融合技术和算法的最新进展。因此，本文提供了用于传感器融合目标检测的硬件和软件方法的端到端综述。最后，我们强调了传感器融合领域的一些挑战，并为自动驾驶系统提出了可能的未来研究方向。
