A systematic review of perception system and simulators for autonomous vehicles research
View/ Open
Share
Metadata
Show full item recordAuthor
Rosique Contreras, María Francisca; Navarro Lorente, Pedro Javier; Fernández Andrés, José Carlos; Padilla Urrea, Antonio ManuelResearch Group
División de Sistemas e Ingeniería Electrónica (DSIE)Knowledge Area
Teoría de la Señal y las ComunicacionesSponsors
This work was partially supported by DGT (ref. SPIP2017-02286) and GenoVision (ref. BFU2017-88300-C2-2-R) Spanish Government projects, and the “Research Programme for Groups of Scientific Excellence in the Region of Murcia" of the Seneca Foundation (Agency for Science and Technology in the Region of Murcia – 19895/GERM/15).Publication date
2019-02-05Publisher
MDPIBibliographic Citation
Rosique, F.; Navarro, P.J.; Fernández, C.; Padilla, A. A systematic review of perception system and simulators for autonomous vehicles research. Sensors 2019, 19, 648.Keywords
Autonomous vehiclePerception system
Simulator
LiDAR
Model based design
Abstract
This paper presents a systematic review of the perception systems and simulators for autonomous vehicles (AV). This work has been divided into three parts. In the first part, perception systems are categorized as environment perception systems and positioning estimation systems. The paper presents the physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.). Furthermore, their strengths and weaknesses are shown, and the quantification of their features using spider charts will allow proper selection of different sensors depending on 11 features. In the second part, the main elements to be taken into account in the simulation of a perception system of an AV are presented. For this purpose, the paper describes
simulators for model-based development, the main game engines that can be used for simulation, simulators from the robotics field, and lastly ...
Collections
- Artículos [861]
The following license files are associated with this item:
Social media