Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle

Nowadays, an autonomous vehicle is one of the fastest-growing technologies. In its movements, the autonomous vehicle requires a good navigation system to run on the specified lane. One sensor that is often used in navigation systems is the camera. However, this camera is constrained by the process and its reading, especially to detect roads that are suitable for the vehicle's position. Thus, this research was conducted to detect the road and distance of nearby objects using the HSV color space method. From the test results, this research succeeded in detecting roads with an accuracy of 78.012 %, and an accuracy of 80% for the safe/unsafe area detection. The results also showed that the method achieved an accuracy of 80% and 74.76%for object detection and object distance detection, respectively. The results of this research implied that the HSV method wasquite good with fairly high accuracy to detect roads and vehicles

Road and Vehicles Detection System Using HSV Color Space for Autonomous Vehicle

Nowadays, an autonomous vehicle is one of the fastest-growing technologies. In its movements, the autonomous vehicle requires a good navigation system to run on the specified lane. One sensor that is often used in navigation systems is the camera. However, this camera is constrained by the process and its reading, especially to detect roads that are suitable for the vehicle's position. In addition, the road without lane-markings is difficult to be detected. Thus, this research was conducted to detect the road without lane-markings and distance of nearby objects using the Hue, Saturation, and Value (HSV) color space method. This HSV method is widely used to detect objects by considering the color of the object. From the test results, this research succeeded in detecting roads with an accuracy of 79.02 %, and an accuracy of 80% for the safe/unsafe area detection. The results also showed that the method achieved an accuracy of 80% and 74.76% for object detection and object distance detection, respectively. The results of this research implied that the HSV method was quite good with fairly high accuracy to detect roads and vehicles

Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behavior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver

Detection and Recognition of Traffic Signs Inside the Attentional Visual Field of Drivers

Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver\u27s 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver

Estudio y análisis de técnicas de identificación de líneas de carretera para guiado autónomo

En este trabajo se aborda el problema de la detección e identificación de las líneas de la calzada para ser aplicado a la conducción autónoma de vehículos. El problema se aborda desde el punto de vista de los métodos clásicos de visión por computador. Nuestro objetivo principal es desarrollar un algoritmo propio dedicado a esta tarea, aprovechando principios fundamentales de procesamiento de imágenes y visión computacional. A lo largo de esta investigación, exploramos diversas metodologías empleadas en desarrollos anteriores y evaluamos la efectividad de nuestro enfoque en comparación con ellas. Este trabajo me ha permitido ampliar de forma significativa mis conocimientos respecto a la visión artificial, así como al mundo de los coches autónomos. He logrado adquirir una comprensión más profunda acerca de este tema y espero que mi trabajo le sea de utilidad a terceros.In this work, we delve into the intriguing field of road lane detection, focusing on a classical approach without resorting to neural networks. Our main objective is to develop our algorithm dedicated to this task, leveraging fundamental principles of image processing and computer vision. Throughout this research, we explore various methodologies used in prior developments and evaluate the effectiveness of our approach in comparison with them. This work represents a significant step towards a deeper understanding and continuous improvement in road lane detection, a crucial component in the field of autonomous driving.Universidad de Sevilla. Grado en Ingeniería Electrónica, Robótica y Mecatrónic

LaneMapper: A City-scale Lane Map Generator for Autonomous Driving

Autonomous vehicles require lane maps to help navigate from a start to a goal position in a safe, comfortable and quick manner. A lane map represents a set of features inherent to the road, such as lanes, stop signs, traffic lights, and intersections. We present a novel approach to detect multiple lane boundaries and traffic signs to create a 3D city-scale map of the driving environment. We detect, recognize and track lane boundaries with multimodal sensory and prior inputs, such as camera, LiDAR, and GPS/IMU, to assist autonomous driving. We detect and classify traffic signs from the image considering high reflectivity of LiDAR points and further register the locations of traffic signs and lane boundaries together in the world coordinate frame. We have also made our code base open-source for the research community to tweak or use our algorithm for their purposes