scholarly journals SEMI-OTOMATIS SISTEM PENGEREMAN AUTONOMOUS VEHICLE MENGGUNAKAN PNEUMATIK SILINDER BERBASIS MIKROKOTROLLER

2021 ◽  
Vol 16 (2) ◽  
pp. 1
Author(s):  
Teguh Arifianto ◽  
Royyan Ghozali ◽  
Akhwan Akhwan ◽  
Sunardi Sunardi ◽  
Willy Artha Wirawan
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicle

Autonomous vehicle merupakan moda transportasi masa depan yang menerapkan imageprocessing dan computer vision untuk pengenalan objek maupun kendali pada motor. Transportasiini dapat beroperasi sendiri sehingga kendaraan ini mengutamakan keamanan dalam berkendara.Jika pada kendaraan konvensional, sistem pengereman dikendalikan oleh pengendara. Namun, padakendaraan autonomous, sistem pengereman akan bekerja pada keadaan tertentu. Penelitian inimemodifikasi pengereman mekanik yang ada pada kendaraan autonomous agar dapat bekerja secarasemi otomatis dengan menggunakan aktuator pneumatik silinder berukuran 50x50mm. Selain itu,penelitian ini juga menggunakan sensor warna pixycam dan micro lidar vl53l0x sebagai input padamikrokontroller arduino. Input pada mikrokontroller arduino ini menjadi parameter perintah untukmengaktifkan selenoid valve agar udara bertekenan dapat menggerakkan pneumatik silinder. Hasildari penelitian ini adalah pemakaian silinder pneumatik berukuran 50x50mm dapat menarik pedalrem pada sarana autonomous dengan tangki udara bervolume 0,16m3 dan tekanan sebesar 6 bar.

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

AUTONOMOUS VEHICLE USING COMPUTER VISION AND LiDAR

2021 ◽  
Vol 9 (2) ◽  
pp. 7
Author(s):  
PAGIRE VRUSHALI ◽  
MATE SANJAY ◽  
◽  
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicle

scholarly journals Mechanical System Control by RGB-D Device

Machines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Chiara Cosenza ◽  
Armando Nicolella ◽  
Daniele Esposito ◽  
Vincenzo Niola ◽  
Sergio Savino
Keyword(s):  
Computer Vision ◽  
Data Acquisition ◽  
Mechanical System ◽  
Control Strategies ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Moving Object ◽  
System Control ◽  
Growing Domain ◽  
3D Camera

Computer vision for control is a growing domain of research and it is widespread in industry and the autonomous vehicle field. A further step is the employment of low-cost cameras to perform these applications. To apply such an approach, the development of proper algorithms to interpret vision data is mandatory. Here, we firstly propose the development of an algorithm to measure the displacement of a mechanical system in contactless mode. Afterwards, we show two procedures that use a 3D camera as a feedback in control strategies. The first one aims to track a moving object. In the second one, the information gained from vision data acquisition allows the mechanical system control to ensure the equilibrium of a ball placed on a moving slide.

scholarly journals Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7267
Author(s):  
Luiz G. Galvao ◽  
Maysam Abbod ◽  
Tatiana Kalganova ◽  
Vasile Palade ◽  
Md Nazmul Huda
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Detection Performance ◽  
The Past ◽  
Perception System ◽  
Good Detection

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

scholarly journals Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection

2021 ◽  
Vol 2128 (1) ◽  
pp. 012020
Author(s):  
Essam M. Abd Elhamied ◽  
Sherin M. Youssef
Keyword(s):  
Computer Vision ◽  
Urban Areas ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Data Traffic ◽  
Remotely Sensed Data ◽  
Urban Scenes ◽  
Cloud Data ◽  
Detection Of Objects ◽  
Distance Data

Abstract Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.

Controlling the autonomous vehicle using computer vision and cloud server

2020 ◽  
Author(s):  
S.V. Aswin Kumer ◽  
L.S.P. Sairam Nadipalli ◽  
P. Kanakaraja ◽  
K. Sarat Kumar ◽  
K.Ch. Sri Kavya
Keyword(s):  
Computer Vision ◽  
Autonomous Vehicle ◽  
Cloud Server
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