[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

Wang, Biyao; Yong, Han; Tian, Di; Guan, Tian

doi:10.1155/2021/8199361

Cited by 11 publications

(4 citation statements)

References 91 publications

(86 reference statements)

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“…Target detection and localization in three-dimensional space is an important basis for people to perceive the environment and has important applications in robotics, autonomous driving, security monitoring, industrial production, intelligent agriculture, and many other fields [11]. And because such sensors as cameras are vulnerable to external factors such as light and weather, and it is too difficult to infer three-dimensional information of the target from two-dimensional images, these algorithms are difficult to achieve satisfactory results in obtaining three-dimensional detection frames of the target [12].…”

Section: Introductionmentioning

confidence: 99%

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Zhen

2022

Journal of Function Spaces

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Unmanned technology is an important development project of today’s cutting-edge science and technology, which has a significant impact on social and economic development, national defense construction, and scientific and technological development. The rapid development of industrial information technology has driven the unmanned intelligent vehicle system to innovate and gradually enter the public’s view, and at the same time, the driving safety of unmanned intelligent vehicles is also widely concerned. Target information perception system is the foundation of unmanned system and the fundamental guarantee of safety and intelligence of unmanned vehicles. There are three key problems of target recognition in unmanned driving, namely, target classification, localization, and attitude determination. In the implementation of a networked virtual environment system, a flexible and complete perception model is needed as the guiding model of the system. Since 3D point cloud data can provide more spatial information than 2D RGB image data, it is more beneficial to determine the target category, position, and pose in 3D. In this paper, based on the existing research of unmanned intelligent vehicle perception system, we combine the application of fusion of 3D target information perception model and develop a nighttime unmanned system based on multiview fusion of 3D target information perception model. This system can simultaneously perform the detection of multiple categories of objects and predict the center point, length, width, height, and orientation of the objects, so that the unmanned car can sense the location of the surrounding objects when driving in the actual scene.

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Section: Introductionmentioning

confidence: 99%

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Zhen

2022

Journal of Function Spaces

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“…Microwave perception is based on the distance information collected by microwave radar, using the distance-correlation algorithm to process and recognize the environment information. A comparison of several perception technologies is shown in Table 1 [ 40 ].…”

Section: Problem Formulation and Process Constructionmentioning

confidence: 99%

Analysis of Lane-Changing Decision-Making Behavior of Autonomous Vehicles Based on Molecular Dynamics

Zhang²,

Song³

et al. 2022

Sensors

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Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

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“…Accurate and reliable environment perception technology guarantees the safety of automatic driving vehicles. Currently, there are four mainstream perception technologies: Machine Vision, LiDAR, Millimeter-Wave Radar, and Infrared Sensor [2]. Visual perception is based on the image information collected by the camera, which is processed using vision-related algorithms to recognize the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based visual perception and decision-making technology for autonomous vehicles

Zhao

2024

ACE

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Along with the social fast developing and the scientific and technological advancement day by day, the automatic driving system has been widely used in many areas, so it has been a hotspot. Because of the constant progress of the deep learning technique, the vision environmental sensor technique and the behavior decision technique have obtained some inspiration. The paper introduces a new method for detecting target with human body, which is based on recurrent convolution neural networks. In recent years, we have seen a number of developments in the field of decision making, such as reinforcement learning, and the development of deep neural networks. This thesis mainly focuses on the research of VMI and decision algorithms, and gives a brief introduction to their further application in ATS. In this paper, some key techniques of vision sensing and behavior decision are discussed in detail. This paper analyses the merits and deficiencies of current study on environment sensing technique and behavior decision technique. Finally, this paper summarizes and forecasts the vision environmental awareness and behavior decision techniques.

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[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

Cited by 11 publications

References 91 publications

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Analysis of Lane-Changing Decision-Making Behavior of Autonomous Vehicles Based on Molecular Dynamics

Deep learning based visual perception and decision-making technology for autonomous vehicles

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