Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion

Ali, Mohammed A. H.; Mailah, Musa; Jabbar, Waheb A.; Moiduddin, Khaja; Ameen, Wadea; Alkhalefah, Hisham

doi:10.3390/s20133694

Cited by 15 publications

(12 citation statements)

References 24 publications

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“…On the one hand, the classical sensor fusion algorithms, such as knowledge-based methods, statistical methods, probabilistic methods, et cetera, utilize the theories of uncertainty from data imperfections, including inaccuracy and uncertainty to fuse sensor data. Reference [ 186 ] proposes a real-time roundabout detection and navigation system in a road environment utilizing a combination of the proposed “Laser Simulator” algorithm to detect objects and the knowledge-based fuzzy logic (FL) algorithm for decision making.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

484

133

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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.

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Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

484

133

View full text Add to dashboard Cite

show abstract

“…knowledge-based methods, statistical methods, probabilistic methods, et cetera, utilize the theories of uncertainty from data imperfections, namely inaccuracy and uncertainty to fuse sensor data. Reference [183] presents a real-time roundabout detection and navigation system in a road environment using a combination of the proposed "Laser Simulator" algorithm to detect obstacles and the classical knowledge-based fuzzy logic (FL) algorithm for decision making.…”

Section: Sensor Fusion Techniques and Algorithmsmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

148

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The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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“…The main scope of this paper is focused on applications that seek to smart cities [45], [46], urban environment monitoring [47], [48], autonomous vehicles [49], [50], advanced driver assistance systems (socalled ADAS) [51], [52], robotic vision systems [53], [54], visual sensor systems [55], risk analysis [56], [57], intelligent traffic flow and analysis [6], [58].…”

Section: Introductionmentioning

confidence: 99%

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

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Privacy of people is a key factor in surveillance systems. Video camera brings us welloff color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

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Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion

Cited by 15 publications

References 24 publications

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

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