Intelligent Driving Assistant based on Road Accident Risk Map Analysis and Vehicle Telemetr

El estudio espuesto a continuación presenta el desarrollo de un asistente inteligente de conducción basado en telemetría vehicular y análisis de mapas de riesgo de accidentalidad vial, cuya responsabilidad es alertar al conductor conforme se lleva a cabo el proceso de conducción para evitar de esta forma situaciones riesgosas que puedan ocasionar accidentes de tránsito. El asistente inteligente a bordo del vehículo reproduce alertas visuales-auditivas en tiempo real de acuerdo a la información obtenida de ambas fuentes, y las conducciones realizadas para su desarrollo y evaluación son obtenidas por un automóvil real en un entorno real. Como resultado, se obtuvo un agente de asistencia inteligente basado en el razonamiento difuso, que apoya correctamente al conductor en tiempo real de acuerdo a los datos de telemetría, al entorno vehicular y a los principios de prácticas seguras de conducción y regulación de transporte.

Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented. Document type: Articl

Design of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment

Purpose – For the purpose of reducing the incidence of hazardous materials transport accident, eliminating the potential threats and ensuring their safety, aiming at the shortcomings in the process of current hazardous materials transportation management, this paper aims to construct the framework of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment. Design/methodology/approach – The system takes the intelligent connected vehicle as the main supporter, integrating GIS, GPS, eye location, GSM, networks and database technology. Findings – By analyzing the transportation characteristics of hazardous materials, this system consists of five subsystems, which are vehicle and driver management subsystem, dangerous sources and hazardous materials management subsystem, route analysis and optimization subsystem, early warning and emergency rescue management subsystem, and basic information query subsystem. Originality/value – Hazardous materials transportation safety management system includes omnibearing real-time monitoring, timely updating of system database, real-time generation and optimization of emergency rescue route. The system can reduce the transportation cost and improve the ability of accident prevention and emergency rescue of hazardous materials

Research on End-to-End Traffic Signs Recognition

伴随着汽车保有量的快速增长，智能化的驾驶辅助系统获得了广泛关注。交通标志识别作为智能交通系统（IntelligentTransportationsystem，ITS）的一个重要组成部分，在上个世纪70年代即开始了相关研究。基于视觉内容的交通标志识别研究虽然经历了近半个世纪，但该问题仍然没有被很好的解决，特别是面向实际应用的真实自然场景的交通标志检测与识别成为模式识别领域的难点之一。由于受复杂的道路状况、背景干扰、天气和光照等因素影响，以及交通标志的种类繁多且类别间相似度高，都为检测和识别带来了极大的挑战。本文深入分析了自然场景下影响交通标志识别的两个关键因素：特征和分类器，综合考虑了交通标志的...Along with the rapid growth of car ownership ,intelligent driving assistant system gained widespread attention . Traffic sign recognition as an important part of Intelligent Transportation System (ITS) has already begun the related research in the 1970s. The research on the traffic sign recognition go through nearly half a century in the field of computer vision, but the problem still doesn’t work...学位：工学硕士院系专业：信息科学与技术学院_计算机系统结构学号：2302010115305

利用模糊語意影像特徵表示法及FPGA平台之車內人臉偵測與定位研究;A Face Detection and Localization System for Driver Using Semantic-based Vague Image Representation and a FPGA Platform

[[abstract]]近幾年來，台灣車輛肇事率逐年攀升，其事故發生原因多為駕駛未注意前方車輛行駛狀況所致。因此，近年來各大車廠陸續開發出可事先偵測並提醒駕駛者是否專心開車的預警系統，以期有效降低此類交通事故的發生。其中，以利用人臉來判斷駕駛之專注情況最值得研究。然而，由於車內空間有限，導致人臉偵測之運算能力也隨之受限。為此，本研究專注於開發一套低體積、低功耗之人臉追蹤系統，以便未來對人臉姿態及表情做進一步之分析。在本研究計畫中，系統首先利用數位相機取得人臉影像，接著採用模糊語意影像特徵表達法(Semantic-based Vague Image Representation, SVIR) 來完成人臉特徵辨識及追蹤。此演算法之特色在於以更少的運算資源，就能夠有效地達到人臉追蹤之目的。而精簡之演算過程，更能幫助本研究實現在系統單晶片之上，進而達到體積小、低功耗以及即時處理之效能。在晶片設計方面，則是採用現場可程式邏輯閘陣列(Field Programmable Gate Array, FPGA)晶片做為測試開發平台，以符合體積小與低開發成本之目的，以期能讓此系統未來能夠更加普及化。最後，在本論文的實驗結果中，本系統設計除了達成人臉偵測與定位之目的，系統資源使用量也僅占晶片運算資源的6.24%，消耗286.83 mW的功率。其功耗少於傳統偵測法使用的高耗能處理器，不但符合先前的研究目標，精簡化的運算過程也使得本系統能在低體積且低性能的晶片上達成高解析度和即時處理。因此，本研究的新式設計有助於未來基於嵌入式裝置的人臉偵測系統之發展。 The traffic accident in Taiwan has been gradually increased in recent years by distraction to vehicle in front. To improve this issue, many car manufacturers have developed various assistant systems to warn driver before accident. However, the lower computing power constrained by narrow space in car is a critical problem for developing an intelligent driving assistant system. For this, this research focuses on the feasibility for designing a compact, low-power, and low-cost face tracking for driver, which is helpful to monitor driver’s condition in real-time.In this research, a digital camera is employed to collect driver’s face. Meanwhile, a novel and concise algorithm of semantics-based vague image representation (SVIR) is also adopted to abstract driver’s face features in order to implement entire system on a single chip. Here a field-programmable gate array (FPFA) developing platform is working as a test bed in which it demonstrates the feasibility for a low-power and low-cost face tracking system.According to the experimental results, the proposed system has a lower power consumption at 286.83mW with only 6.24% hardware resource usage of chip. With this design, driver’s face can be effectively tracked during the scanning of image pixels. Such real-time performance is workable and promising for drive’s assistant system in the future