scholarly journals Using Real-Time Dynamic Prediction to Implement IoV-Based Collision Avoidance

2019 ◽  
Vol 9 (24) ◽  
pp. 5370
Author(s):  
Che-Cheng Chang ◽  
Wei-Ming Lin ◽  
Chuan-An Lai
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Confidence Region ◽  
Dynamic Prediction ◽  
Internet Of Vehicles ◽  
Time Dynamic ◽  
The Future ◽  
Future Position

For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle.

scholarly journals Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps

2019 ◽  
pp. 567-582 ◽  
Author(s):  
B. Ravi Kiran ◽  
Luis Roldão ◽  
Beñat Irastorza ◽  
Renzo Verastegui ◽  
Sebastian Süss ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Dynamic Object ◽  
Time Dynamic

REAL-TIME SELF-COLLISION AVOIDANCE SYSTEM FOR ROBOTS USING ROBE

2004 ◽  
Vol 01 (03) ◽  
pp. 533-550 ◽  
Author(s):  
FUMI SETO ◽  
KAZUHIRO KOSUGE ◽  
YASUHISA HIRATA
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Reaction Force ◽  
Collision Avoidance System ◽  
Representation Method ◽  
Elastic Elements

In this paper, we propose a real-time self-collision avoidance system for robots which cooperate with a human/humans. First, the robot is represented by elastic elements. The representation method is referred to as RoBE (Representation of Body by Elastic elements). Elastic balls and cylinders are used as the elements to simplify collision detection, although elements of any shape could be used for RoBE. When two elements collide with each other, a reaction force is generated between them, and self-collision avoidance motion is generated by the reaction force. Experiments using the mobile robot with dual manipulators, referred to as MR Helper, illustrate the validity of the proposed system.

A Method for Searching Optimal Routes with Collision Avoidance on Raster Charts

2003 ◽  
Vol 56 (3) ◽  
pp. 371-384 ◽  
Author(s):  
Ki-Yin Chang ◽  
Gene Eu Jan ◽  
Ian Parberry
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Linear Time ◽  
Routing Algorithm ◽  
Time And Space ◽  
Navigation Safety

Collision avoidance is an intensive discussion issue for navigation safety. This article introduces a new routing algorithm for finding optimal routes with collision detection and avoidance on raster charts or planes. After the required data structure of the raster chart is initialized, the maze routing algorithm is applied to obtain the particular route of each ship. Those ships that have potential to collide will be detected by simulating the particular routes with ship domains. The collision avoidance scheme can be achieved by using the collision-area-marking method with collision avoidance rules at sea. The algorithm has linear time and space complexities, and is sufficiently fast to perform real-time routing on the raster charts.

scholarly journals Target Detection For Visual Collision Avoidance System

2021 ◽  
Vol 93 (7s) ◽  
pp. 159-166
Author(s):  
Miro Petković ◽  
◽  
Danko Kezić ◽  
Igor Vujović ◽  
Ivan Pavić ◽  
...  
Keyword(s):  
Object Detection ◽  
Target Tracking ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Automatic Identification ◽  
Surveillance Systems ◽  
International Regulations ◽  
Collision Avoidance System ◽  
Small Targets

Automatic Identification Systems (AIS) and Automatic Radar Plotting Aids (ARPA) are commonly used to detect targets for collision avoidance. However, AIS cannot detect targets without AIS transmitters and ARPA has limitations due to blind sector and small targets may not be detected. Advances in computer performance and video-based detection generated much interest in developing intelligent video surveillance systems to achieve autonomous navigation. To develop a reliable collision avoidance system, we propose the use of a visual camera for real-time object detection and target tracking. Moreover, the system should follow the International Regulations for Preventing Collisions at Sea (COLREGs) to avoid catastrophic accidents. In this paper only a part of the system is presented. For real-time object detection, the You Only Look Once (YOLO) ver. 3 convolutional neural network is used, and the target tracking filter based on a Kalman filter with built-in estimated relative position and velocity.

scholarly journals MOVING OBJECT DETECTION METHOD OF VIDEO SATELLITE BASED ON TRACKING CORRECTION DETECTION

2020 ◽  
Vol V-3-2020 ◽  
pp. 701-707
Author(s):  
X. Yang ◽  
F. Li ◽  
M. Lu ◽  
L. Xin ◽  
X. Lu ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Target Detection ◽  
Engineering Application ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Monitoring ◽  
Moving Target ◽  
Time Dynamic

Abstract. It is the focus of current research that how to realize high precision and real-time dynamic monitoring and tracking of moving targets by video satellites because of instantaneous and dynamic continuous observation of targets in a certain area by the video satellites. The existing detection and tracking methods for moving objects have target misdetection and missed detection, which reduces the accuracy of moving object detection. In this paper, a Tracking Correction Detection Correction (TCD) method is proposed to solve these problems. Firstly, the background model is established by using the improved ViBe target detection algorithm, and the moving target mask is obtained by adaptive threshold calculation. By using pyramid structure iterative algorithm, the moving object can be classified as noise or real object according to the set of detection results of different detection windows. The high-order correlation vector tracking method is used to modify the detection result of the moving target acquired in the previous frame, and finally the accurate detection result of the moving target is obtained. The comparison analysis between the frame difference (FD) method, GMM method, ViBe method and TCD method shows that the TCD method has better robustness for noise, light and background dynamic changes, and the test results of TCD method are more complete and the real-time is better. It is proved by this work that the accuracy of the target detection of TCD method has reached 85%, which has a high engineering application value.

Real-time dynamic prediction model of NOx emission of coal-fired boilers under variable load conditions

Fuel ◽  
2020 ◽  
Vol 274 ◽  
pp. 117811 ◽  
Author(s):  
Tingting Yang ◽  
Kangfeng Ma ◽  
You Lv ◽  
Yang Bai
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Nox Emission ◽  
Variable Load ◽  
Dynamic Prediction ◽  
Time Dynamic ◽  
Load Conditions

Real-time dynamic system to path tracking and collision avoidance for redundant robotic arms

2016 ◽  
Vol 23 (1) ◽  
pp. 73-96 ◽  
Author(s):  
Gao Xin ◽  
Jia Qingxuan ◽  
Sun Hanxu ◽  
Chen Gang ◽  
Zhang Qianru ◽  
...  
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Collision Avoidance ◽  
Path Tracking ◽  
Robotic Arms ◽  
Time Dynamic

scholarly journals Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1987
Author(s):  
Maoqiang Gu ◽  
Anjun Xu ◽  
Hongbing Wang ◽  
Zhitong Wang
Keyword(s):  
Carbon Content ◽  
Real Time ◽  
Process Parameters ◽  
Short Term Memory ◽  
Integral Model ◽  
Case Based Reasoning ◽  
Prediction Errors ◽  
Converter Steelmaking ◽  
Dynamic Prediction ◽  
Time Dynamic

The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.

REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

SIMULATION ◽  
2021 ◽  
pp. 003754972110047
Author(s):  
Muhammad A Butt ◽  
Faisal Riaz ◽  
Yasir Mehmood ◽  
Somyyia Akram
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Collision Detection ◽  
Autonomous Vehicles ◽  
Affective Computing ◽  
Field Experiments ◽  
Road Traffic ◽  
Sensory System ◽  
Driving Mode ◽  
Human Driver

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.

Sign in / Sign up

Export Citation Format

Share Document