River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking

Chaâri

J Ambient Intell Human Comput

2020

Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also doesn't meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection. Keywords Autonomous driving • embedded camera • road marking • multi-hyperbola • Bayesian framework

Section: The Adopted Modelmentioning

confidence: 99%

Section: Curved Lanesmentioning

confidence: 99%

Bayesian curved lane estimation for autonomous driving

Chaâri

J Ambient Intell Human Comput

2020

“…(2) Drivers; (3) Pedestrians; and (4) Infrastructure which includes roads, traffic signs, traffic lights, and traffic control centres. The ITS sub-layer focuses on some specific data processing and computation for intelligent transportation systems such as traffic sign detection and analysis, vehicle detection and tracking, incident detection, driver inattention such as distraction and fatigue detection, pedestrian detection, lane and object detection [15], etc. Table 8 gives some details of functional units for the ITS sub-layer.…”

Section: Essential Functional Units In the Intelligent Transportatmentioning

confidence: 99%

Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges

et al. 2019

Self Cite

The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario. INDEX TERMS Internet of Vehicles, IoV, layer architecture, smart city, applications, big data.

IEEE/CAA J. Autom. Sinica

“…In [22], a novel parallel-snake model was introduced. In [23], lane boundaries were detected based on a combination of Hough transform in near-field areas and a river-flow method in farfield areas. Finally, lanes were modelled with a B-spline model and tracked with a Kalman filter.…”

Section: B Conventional Image-processing-based Lane Detection Algorimentioning

confidence: 99%

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Xing

Chen

Wang

et al. 2018

146

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.