Distance measurement system for smart vehicles

Zhao, Mingchang; Mammeri, Abdelhamid; Boukerche, Azzedine

doi:10.1109/ntms.2015.7266486

Cited by 19 publications

(10 citation statements)

References 16 publications

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“…Each measurement system has a sensory setup consisting of a camera-LiDAR pair, with the exception of the vehicle which has one camera and two LiDARs. For precise distance measurements and large-scale 3D reconstruction in automotive applications, stereo vision is becoming a less and less viable choice simply due to the precision loss at distances that are relevant to driving (compared to the high precision and falling prices of LiDAR technology) [11].…”

Section: Overview Of the Central Perception System Architecturementioning

confidence: 99%

Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud

et al. 2021

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We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

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Section: Overview Of the Central Perception System Architecturementioning

confidence: 99%

Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud

et al. 2021

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“…Щоб уникнути зіткнення транспортних засобів, необхідно дотримуватися безпечної відстані до різних об'єктів. Зараз для оцінювання відстані використовують вимірювальні технології, як-от ультразвукове визначення відстані, радар міліметрового діапазону, LIDAR (Light Identification, Detection and Ranging), комп'ютерний зір й інфрачервоні прилади визначення відстані [3]. Для підвищення ефективності поєднують різні технології (наприклад, LIDAR і камеру).…”

Section: вступunclassified

High Resolution Camera Design Planning Theoretical Foundations for Driving Systems

Stadnichuk¹,

Kolobrodov²,

Mosolab³

et al. 2021

KPI SN

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Background. Analysis of statistical data showed that in most cases the cause of the accident is driver error and, as a consequence, violation of traffic rules. In this regard, over the past 10 years, active developments in the field of recognition of road signs and other obstacles in the path of a car have been actively developing. Car manufacturers offer ready-made built-in systems, mounted behind the interior rearview mirror and connected to the car’s on-board computer, which carries out further control of the car in a critical situation. The main disadvantage of these systems of this class is the low range of recognition of road signs, the dependence of optical parameters on temperature and low light sensitivity. Objective. The purpose of the paper is to model an athermal objective for a high-resolution camera, investigate the characteristics of lenses depending on the ambient temperature. Methods. Analysis and modeling of objectives, lenses, optical glass from different materials. Results. A high-resolution camera objective for all types of cars is proposed. An athermal objective was developed for a high-resolution camera. Conclusions. The optimized athermal design of the visible spectrum objective for long-range car cameras is considered. Car cameras typically have a fixed focus, and forward-facing cameras typically require relatively long focal lengths to provide information about distant objects. The optical system for these cameras should provide high resolution, as well as operate in a wide range of ambient temperatures. The camera design parameters are derived from the functional requirements of road sign recognition at a distance of 200 m. The objective design has five lenses with spherical surfaces. The objective has a relative aperture of f/2 and a modulation transfer function (MTF) of more than 0.5 at 111 l/mm over the entire temperature range.

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“…Measurement of the distance to objects is generally based on a range of complementary sensors: radar [1], LiDAR [2], or time-of-flight (ToF) camera [3]. However, these methods have some limitations because they are often expensive and bulky.…”

Section: Introductionmentioning

confidence: 99%

Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

et al. 2021

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For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI’s road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.

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Distance measurement system for smart vehicles

Cited by 19 publications

References 16 publications

Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud

Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud

High Resolution Camera Design Planning Theoretical Foundations for Driving Systems

Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

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