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

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 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.

Autonomous Vehicles and Avoiding the Trolley (Dilemma): Vehicle Perception, Classification, and the Challenges of Framing Decision Ethics

“…Environmental perception is achieved by acquiring and converting raw data from an on-board suite of vehicle sensors into scene understanding. Such sensors include cameras, stereovision, infrared, radar, ultrasonic (Van Brummelen et al 2018;Rosique et al 2019), and light detection and radar (LIDAR). This section therefore delineates how AVs assimilate the environment through vision and LiDAR-based sensing.…”

“…LIDAR significantly enhances AV perception capabilities (Schwarz 2010), as LIDAR sensors rotate at high speeds, emitting laser beams to create a sparse 3D point cloud, with each data point signifying a reflection from an objects surface (Rosique et al 2019). LIDAR is central for object detection and distance estimation but is limited with regard to object recognition.…”

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

“…In addition to using video cameras as major visionary sensors, these vehicles also use other sensors for detection of different events in the car's surroundings, e.g., RADAR and LIDAR. The surrounding environment of the autonomous vehicles is perceived in two stages [74]. In the first stage, the whole road is scanned for the detection of changes in the driving conditions such as traffic signs and lights, pedestrian crossing, and other obstacles, etc.…”

Adaptive Multi-Modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications

“…Connected autonomous electrified vehicles (CAEVs) offer high potential to improve road safety, boost traffic efficiency and minimize carbon emissions [1], as well as reduce vehicle wear, transportation times and fuel consumption [2]. Perception systems in CAEVs [3] are the fundamental of decision making, route planning, obstacle avoidance and trajectory tracking [4], etc. As the most essential sensor of perception systems, the visual camera  Guo-Dong Yin ygd@seu.edu.cn…”