A Smart System for Detection of Road Lane and Divider

Road Lane and divider detection is a core part in environmental perception for driver assistance system. The paper discusses about machine learning and computer vision approach to identify road lanes and dividers and proposes a system for detection of road lane and divider. Driving assistance systems heavily rely on the road lane and divider recognition. Voting classification was implemented using 7 different classifiers. The combination of Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) provided feature extraction. Principle Component Analysis (PCA) provided dimension reduction. The performance has been examined on the basis of accuracy (95.50%), precision (78.81%), recall (65.71%), and F1 score (71.67%). The proposed solution is helpful in solving issues related to road safety and reducing road accidents

Driving Assistance System with Lane Change Detection

In this study, a simple technology for a self-driving system called “driver assistance system” is developed based on embedded image identification. The system consists of a camera, a Raspberry Pi board, and OpenCV. The camera is used to capture lane images, and the image noise is overcome through color space conversion, grayscale, Otsu thresholding, binarization, erosion, and dilation. Subsequently, two horizontal lines parallel to the X-axis with a fixed range and interval are used to detect left and right lane lines. The intersection points between the left and right lane lines and the two horizontal lines can be obtained, and can be used to calculate the slopes of the left and right lanes. Finally, the slope change of the left and right lanes and the offset of the lane intersection are determined to detect the deviation. When the angle of lanes changes drastically, the driver receives a deviation warning. The results of this study suggest that the proposed algorithm is 1.96 times faster than the conventional algorithm

A monocular color vision system for road intersection detection

Urban driving has become the focus of autonomous robotics in recent years. Many groups seek to benefit from the research in this field including the military, who hopes to deploy autonomous rescue forces to battle-torn cities, and consumers, who will benefit from the safety and convenience resulting from new technologies finding purpose in consumer automobiles. One key aspect of autonomous urban driving is localization, or the ability of the robot to determine its position on a road network. Any information that can be obtained for the surrounding area including stop signs, road lines, and intersecting roads can aid this localization. The work here attempts to combine some previously established computer vision methods to identify roads and develop a new method that can identify both the road and any possible intersecting roads present in front of a vehicle using a single color camera. Computer vision systems rely on a few basic methods to understand and identify what they are looking at. Two valuable methods are the detection of edges that are present in the image and analysis of the colors that compose the image. The method described here attempts to utilize edge information to find road lines and color information to find the road area and any similarly colored intersecting roads. This work demonstrates that combining edge detection and color analysis methods utilizes their strengths and accommodates for their weaknesses and allows for a method that can successfully detect road lanes and intersecting roads at speeds fast enough for use with autonomous urban driving

Ripple-GAN: Lane line detection with Ripple Lane Line Detection Network and Wasserstein GAN

With artificial intelligence technology being advanced by leaps and bounds, intelligent driving has attracted a huge amount of attention recently in research and development. In intelligent driving, lane line detection is a fundamental but challenging task particularly under complex road conditions. In this paper, we propose a simple yet appealing network called Ripple Lane Line Detection Network (RiLLD-Net), to exploit quick connections and gradient maps for effective learning of lane line features. RiLLD-Net can handle most common scenes of lane line detection. Then, in order to address challenging scenarios such as occluded or complex lane lines, we propose a more powerful network called Ripple-GAN, by integrating RiLLD-Net, confrontation training of Wasserstein generative adversarial networks, and multi-target semantic segmentation. Experiments show that, especially for complex or obscured lane lines, Ripple-GAN can produce a superior detection performance to other state-of-the-art methods

Perception and intelligent localization for autonomous driving

Mestrado em Engenharia de Computadores e TelemáticaVisão por computador e fusão sensorial são temas relativamente recentes, no entanto largamente adoptados no desenvolvimento de robôs autónomos que exigem adaptabilidade ao seu ambiente envolvente. Esta dissertação foca-se numa abordagem a estes dois temas para alcançar percepção no contexto de condução autónoma. O uso de câmaras para atingir este fim é um processo bastante complexo. Ao contrário dos meios sensoriais clássicos que fornecem sempre o mesmo tipo de informação precisa e atingida de forma determinística, as sucessivas imagens adquiridas por uma câmara estão repletas da mais variada informação e toda esta ambígua e extremamente difícil de extrair. A utilização de câmaras como meio sensorial em robótica é o mais próximo que chegamos na semelhança com aquele que é o de maior importância no processo de percepção humana, o sistema de visão. Visão por computador é uma disciplina científica que engloba àreas como: processamento de sinal, inteligência artificial, matemática, teoria de controlo, neurobiologia e física. A plataforma de suporte ao estudo desenvolvido no âmbito desta dissertação é o ROTA (RObô Triciclo Autónomo) e todos os elementos que consistem o seu ambiente. No contexto deste, são descritas abordagens que foram introduzidas com fim de desenvolver soluções para todos os desafios que o robô enfrenta no seu ambiente: detecção de linhas de estrada e consequente percepção desta, detecção de obstáculos, semáforos, zona da passadeira e zona de obras. É também descrito um sistema de calibração e aplicação da remoção da perspectiva da imagem, desenvolvido de modo a mapear os elementos percepcionados em distâncias reais. Em consequência do sistema de percepção, é ainda abordado o desenvolvimento de auto-localização integrado numa arquitectura distribuída incluindo navegação com planeamento inteligente. Todo o trabalho desenvolvido no decurso da dissertação é essencialmente centrado no desenvolvimento de percepção robótica no contexto de condução autónoma.Computer vision and sensor fusion are subjects that are quite recent, however widely adopted in the development of autonomous robots that require adaptability to their surrounding environment. This thesis gives an approach on both in order to achieve perception in the scope of autonomous driving. The use of cameras to achieve this goal is a rather complex subject. Unlike the classic sensorial devices that provide the same type of information with precision and achieve this in a deterministic way, the successive images acquired by a camera are replete with the most varied information, that this ambiguous and extremely dificult to extract. The use of cameras for robotic sensing is the closest we got within the similarities with what is of most importance in the process of human perception, the vision system. Computer vision is a scientific discipline that encompasses areas such as signal processing, artificial intelligence, mathematics, control theory, neurobiology and physics. The support platform in which the study within this thesis was developed, includes ROTA (RObô Triciclo Autónomo) and all elements comprising its environment. In its context, are described approaches that introduced in the platform in order to develop solutions for all the challenges facing the robot in its environment: detection of lane markings and its consequent perception, obstacle detection, trafic lights, crosswalk and road maintenance area. It is also described a calibration system and implementation for the removal of the image perspective, developed in order to map the elements perceived in actual real world distances. As a result of the perception system development, it is also addressed self-localization integrated in a distributed architecture that allows navigation with long term planning. All the work developed in the course of this work is essentially focused on robotic perception in the context of autonomous driving

Lane detection in autonomous vehicles : A systematic review

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-Alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-The-Art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

[EN] Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1).Muhammad, K.; Ullah, A.; Lloret, J.; Del Ser, J.; De Albuquerque, VHC. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 22(7):4316-4336. https://doi.org/10.1109/TITS.2020.30322274316433622

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs