Road Boundary Detection Based on Random Forests and Particle Filter

2014 ◽  
Vol 687-691 ◽  
pp. 3875-3879
Author(s):  
Da Wei Zhao ◽  
Tao Wu ◽  
Yu Qiang Fang ◽  
Jing Dai
Keyword(s):  
Particle Filter ◽  
Random Forests ◽  
Autonomous Vehicle ◽  
Boundary Detection ◽  
Road Detection ◽  
New Approach ◽  
Point Position ◽  
The Road ◽  
Boundary Slope ◽  
Primary Problem

Road detection is a primary problem for autonomous vehicle. There have been many approaches attempting to solve this problem. However, most of the approaches tend to be affected by shadow, occlusion and not always robust. In this paper, we propose a new approach to detect the road boundary, our approach has two parts: firstly, we detect the road boundary by random forests on the pixel level, then we track the road boundary by particle filter with the parameters of vanish point position and road boundary slope. The experiment shows our approach is an effective way to detect road boundary fast and robustly.

A SYSTEM FOR DETECTING AND RECOGNIZING MOVING BIOLOGICAL OBJECTS FOR UNMANNED VEHICLES BASED ON INTELLIGENT EDGE COMPUTING

2021 ◽  
Author(s):  
М.А. АЛЬ-СВЕЙТИ ◽  
А.С. МУТХАННА ◽  
А.С. БОРОДИН ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Unmanned Vehicles ◽  
Mesh Network ◽  
New Approach ◽  
Remote Areas ◽  
The Road ◽  
Biological Objects ◽  
Time Critical

Обсуждается возможность применения бортовых платформ с целью поддержки наземных сетей для использования ресурсов автономных транспортных средств как части критичных к задержкам приложений. Бортовые платформы могут повысить безопасность поездок транспортных средств, доставляя на них своевременную информацию об окружающей обстановке даже в отдаленных районах земного шара. Обсуждаются требования и потенциальные решения для поддержки инфраструктуры автономных транспортных средств как части интеллектуальной транспортной системы. Предлагается использовать вдоль дороги энергоэффективные сенсоры, которые могут объединяться друг с другом в Mesh-сети. Кроме того, предлагается новый подход к обнаружению активности биологических объектов на обочине дороги, основанный на технологиях искусственного интеллекта. The article discusses the possibility of using onboard platforms to support the terrestrial networks for autonomous vehicles resources as a part of delay-critical applications. Onboard platforms can improve the safety of vehicle rides by delivering time-critical information about the environment to the vehicles, even in remote areas of the world. In this paper, we discuss requirements and potential solutions for supporting the autonomous vehicle infrastructure, as a part of an intelligent transportation system. It is proposed to use energy-efficient sensors along the road, which can connect with each other in a Mesh network. In addition, a new approach for the detection of biological objects activity on the roadside, based on artificial intelligence technologies is suggested.

Real-time unstructured road detection based on CNN and Gibbs energy function

2021 ◽  
pp. 1-19
Author(s):  
Mingzhou Liu ◽  
Xin Xu ◽  
Jing Hu ◽  
Qiannan Jiang
Keyword(s):  
Gibbs Energy ◽  
Energy Function ◽  
Gaussian Mixture Models ◽  
Texture Features ◽  
Gaussian Mixture ◽  
Gibbs Energy Function ◽  
Road Detection ◽  
The Road ◽  
Detection Algorithms ◽  
Random Sample Consensus

Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels.

Road Detection Based on Edge Feature with GAC Model in Aerial Image

2021 ◽  
Author(s):  
Linying Zhou ◽  
Zhou Zhou ◽  
Hang Ning
Keyword(s):  
Transformation Method ◽  
Aerial Images ◽  
Aerial Image ◽  
Initial Contour ◽  
Detection Accuracy ◽  
Road Detection ◽  
Canny Operator ◽  
The Road ◽  
Gradient Magnitude ◽  
Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

scholarly journals Model Contrast of Autonomous Vehicle Impacts on Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Derek Hungness ◽  
Raj Bridgelall
Keyword(s):  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Sociodemographic Factors ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Traffic Conditions ◽  
Demand Models ◽  
Wide Range ◽  
Potential Benefits

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

scholarly journals Improving V2X-6G network capacity using a new UAV-based approach in a Cloud/ICN architecture, case Study: VANET network

2021 ◽  
Vol 297 ◽  
pp. 01019
Author(s):  
Abdeslam Houari ◽  
Tomader Mazri
Keyword(s):  
Mobile Networks ◽  
Network Capacity ◽  
Intelligent Environment ◽  
New Approach ◽  
The Road ◽  
Road Users ◽  
Aerial Vehicle ◽  
On The Road

6G of mobile networks plays a crucial role in improving the capacity and enhancing the quality of services of Vehicle-to-Everything (V2X) based networks evolving in an intelligent environment. VANET is a promising project in the intelligent transportation field using V2X communications. The emergence of several 5G and 6G technologies has raised several challenges for scientists and researchers to allow vehicles and road users to enjoy several services while ensuring their safety on the road. Among these technologies, the unmanned aerial vehicle (UAV), which can perform different tasks for road users and vehicle drivers such as data caching, packet relaying and processing. In this article, we present a new approach based on 6G Unmanned Aerial Vehicles (UAV) technology on a vehicular cloud architecture while exploiting the exchange support of information-centric networking (ICN) for the improvement of network capacity.

Service-Bonded Inquiry: The Road Less Traveled

1997 ◽  
Vol 17 (1) ◽  
pp. 107-121 ◽  
Author(s):  
Tom Martinek ◽  
Don Hellison
Keyword(s):  
Personal Values ◽  
Information Gathering ◽  
Community Settings ◽  
New Approach ◽  
The Road ◽  
Scholarly Inquiry ◽  
Research Service ◽  
The Way

In this essay, a new approach to doing research in schools and other community settings is described: service-bonded inquiry. This approach allows researchers to expand the boundaries of scholarly inquiry through the integration of service and scholarship. It is not an attempt to replace traditional forms of research; rather, it serves to complement the way researchers have historically conducted research. Service-bonded inquiry is the proverbial bridge between what Hal Lawson (1990) calls information gathering and useful information. The discussion here focuses on describing important assumptions underlying service-bonded inquiry and arguing that personal values and commitment must be assessed before engaging in this type of research. In addition, guideposts for evaluating and doing service-bonded inquiry are provided.

scholarly journals A Low Cost Vision-Based Road-Following System for Mobile Robots

2018 ◽  
Vol 8 (9) ◽  
pp. 1635 ◽  
Author(s):  
Haojie Zhang ◽  
David Hernandez ◽  
Zhibao Su ◽  
Bo Su
Keyword(s):  
Mobile Robots ◽  
Unscented Kalman Filter ◽  
Detection System ◽  
Tracking System ◽  
Low Cost ◽  
Recognition Rate ◽  
Road Detection ◽  
The Road ◽  
Road Following ◽  
Following Error

Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.

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