A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research

By F. Rosique, P. Navarro, C. Fernández, A. Padilla. 2019

410 citations recorded by the source corpus.

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 simulators used specifically for AV. Finally, the current state of regulations that are being applied in different countries around the world on issues concerning the implementation of autonomous vehicles is presented.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “A complete review of sensors and simulators utilised within the autonomous driving community is available in [98] for readers.”
Multi-Sensor Fusion in Automated Driving: A Survey (2020, background): “The difference between them will be wearier, and CMOS is expected to replace CCD [57].”
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, background): “However, a recent study [181,182] revealed that obtaining the current state-of-the-art fusion techniques and algorithms is an arduous and challenging task due to multidisciplinary and variants of proposed fusion algorithms in the literature.”
Synthetic Data for Deep Learning (2019, neutral): “works, we also note other frameworks for multi-agent simulation that have been used for autonomous vehicles, usually without a realistic 3D rendered environment (see also a comparison of the frameworks in [420] and of their perception systems in [503]): JaSIM [188], a multiagent 3D environment model”
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, background): “Though this section provides only a brief description of the simulators, a more systematic review of the topic can be found in [39].”
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions (2021, neutral): “Nevertheless, the research community recognizes the challenge of generating realistic scenarios that mimics the physical world while depending solely on software [8].”