Multi-Sensor Fusion in Automated Driving: A Survey

By Zhangjing Wang, Yu Wu, Qingqing Niu. IEEE Access, 2020

453 citations recorded by the source corpus.

With the significant development of practicability in deep learning and the ultra-high-speed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

Subfields: Aerospace Engineering, Instrumentation

View Paper: https://www.semanticscholar.org/paper/075b654a5c0e9ee23072c449eaaef1ceb980657c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

Citations in the corpus

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection (2022, background): “Multi-sensor fusion [26] is the integration of heterogeneous information collected by different sensors to alleviate the uncertainty and vulnerability of systems that rely on a single sensor.”
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, methodology): “A variety of calibration techniques, [124–126,133,139–142] to cite a few, have been developed to accommodate various applications, such as AVs, Unmanned Surface Vehicle (USV) or underwater 3D reconstructions.”
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review (2020, background): “Previous reviews [12] [13] on deep learning methods for multi-modal data fusion covered a broad range of sensors, including radars, cameras, LiDARs, Ultrasonics, IMU, Odometers, the GNSS and HD Maps.”