Parking Space Recognition for Autonomous Valet Parking Using Height and Salient‐Line Probability Maps

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient‐line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.     

In‐Vehicle AR‐HUD System to Provide Driving‐Safety Information

Augmented reality (AR) is currently being applied actively to commercial products, and various types of intelligent AR systems combining both the Global Positioning System and computer‐vision technologies are being developed and commercialized. This paper suggests an in‐vehicle head‐up display (HUD) system that is combined with AR technology. The proposed system recognizes driving‐safety information and offers it to the driver. Unlike existing HUD systems, the system displays information registered to the driver's view and is developed for the robust recognition of obstacles under bad weather conditions. The system is composed of four modules: a ground obstacle detection module, an object decision module, an object recognition module, and a display module. The recognition ratio of the driving‐safety information obtained by the proposed AR‐HUD system is about 73%, and the system has a recognition speed of about 15 fps for both vehicles and pedestrians.     

Adaptive Multimodal In‐Vehicle Information System for Safe Driving

This paper proposes an adaptive multimodal in‐vehicle information system for safe driving. The proposed system filters input information based on both the priority assigned to the information and the given driving situation, to effectively manage input information and intelligently provide information to the driver. It then interacts with the driver using an adaptive multimodal interface by considering both the driving workload and the driver's cognitive reaction to the information it provides. It is shown experimentally that the proposed system can promote driver safety and enhance a driver's understanding of the information it provides by filtering the input information. In addition, the system can reduce a driver's workload by selecting an appropriate modality and corresponding level with which to communicate. An analysis of subjective questionnaires regarding the proposed system reveals that more than 85% of the respondents are satisfied with it. The proposed system is expected to provide prioritized information through an easily understood modality.     

Privacy theory in practice: designing a user interface for managing location privacy on mobile devices

ABSTRACT

Disclosing the current location of a person can seriously affect their privacy, but many apps request location information to provide location-based services. Simultaneously, these apps provide only crude controls for location privacy settings (sharing all or nothing). There is an ongoing discussion about rights of users regarding their location privacy (e.g. in the context of the General Data Protection Regulation – GDPR). GDPR requires data collectors to notify users about data collection and to provide them with opt-out options. To address these requirements, we propose a set of user interface (UI) controls for fine-grained management of location privacy settings based on privacy theory (Westin), privacy by design principles and general UI design principles. The UI notifies users about the state of location data sharing and provides controls for adjusting location sharing preferences. It addresses three key issues: whom to share location with, when to share it, and where to share it. Results of a user study (N=23) indicate that (1) the proposed interface led to a greater sense of control, that (2) it was usable and well received, and that (3) participants were keen on using it in real life. Our findings can inform the development of interfaces to manage location privacy.     

SPRD:fast application repackaging detection approach in Android based on application’s UI and program dependency graph

A two stage detection approach which combine application’s UI and program code based on the observation that repackaging applications merely modify the structure of their user interface was proposed.Firstly,a fast hash similarity detection technique based on an abstracted representation of UI to identify the potential visual-similar repackaging applications was designed.Secondly,program dependency graph is used to represent as the feature of app to achieve fine-grained and precise code clone detection.A prototype system,SPRD,was implemented based on the proposed approach.Experimental results show that the proposed approach achieves a good performance in both scalability and accuracy,and can be effectively applied in millions of applications and billions of code detection.     

基于PB的本地计费帐务系统界面设计

用户界面是计算机软件和用户交互的接口,是控制和选择信息输入/输出的主要途径,也是衡量软件质量的一个重要标准.首先介绍了用户界面与用户界面设计的原则及工作流程（分为结构设计、交互设计、视觉设计3个部分）,然后探讨本地计费帐务系统中用户界面设计的实现,主要是应用系统的窗口、菜单及各种控件等的设计方法.最后介绍一种通过用户对象定义状态栏的方法.     

基于脑电识别与车辆操纵特征的驾驶疲劳检测分析

本文以脑电识别与车辆操纵特征为切入点,通过模拟疲劳驾驶实验,将脑电识别与车辆操纵特性相结合来检测驾驶员的疲劳状态.通过对脑电信号的S变换分析,发现不同驾驶时刻其变换时频谱图存在显著差异,可用来区分驾驶过程中驾驶员的精神状态,结合车辆操纵特征参数,得到操纵特征与疲劳状态的关系,为脑电识别与操纵特征的驾驶疲劳检测的有效性提供一定的理论和实验基础.     

Exploring Promising Technology in ICT Sector Using Patent Network and Promising Index Based on Patent Information

This research proposes the use of a patent analysis methodology that can suggest promising technology in the ICT sector at the micro‐level. This approach identifies core patents from the technology field, groups them as research frontiers (RFs), and develops a visualized network based on the citing relationships to monitor the relationship among RFs. In addition, it calculates a “promising index” based on the growth potential, impact, and marketability of patents to ultimately derive promising RFs. To illustrate the proposed approach, this research presents analysis results for a chosen area, which is the user interface and user experience (UI/UX) technology field. By proposing promising technological fields at the micro‐level, the proposed methodology will serve as a useful decision‐making support tool in selecting R&D projects, technology planning, and determining technology policy direction.     

Driver activity recognition by learning spatiotemporal features of pose and human object interaction

Detecting hazardous activity during driving can be useful in curbing roadside accidents. Existing techniques utilizing image based features for encoding such activity can sometimes misclassify crucial scenarios. One particular work by Zhao et al. (2013 [1], 2013 [2], 2011 [3]) suggests an image based feature set that encodes the driver’s pose, which is categorized into one of four activities. We bring more clarity in understanding the activity by proposing a richer, video based feature set that adeptly exploits spatiotemporal information of the driver. Our feature set encodes the driver’s pose, crucial variations in pose and interactions with objects within the vehicle. The feature set is tested on our newly created dataset since the ones used in literature are not publicly available. Our proposed feature set captures a larger number of activities and using standard classifiers and benchmarks it has shown significant improvements over the existing ones.     

Study on the Take-over Performance of Level 3 Autonomous Vehicles Based on Subjective Driving Tendency Questionnaires and Machine Learning Methods

Level 3 autonomous vehicles require conditional autonomous driving in which autonomous and manual driving are alternately performed; whether the driver can resume manual driving within a limited time should be examined. This study investigates whether the demographics and subjective driving tendencies of drivers affect the take-over performance. We measured and analyzed the reengagement and stabilization time after a take-over request from the autonomous driving system to manual driving using a vehicle simulator that supports the driver's take-over mechanism. We discovered that the driver's reengagement and stabilization time correlated with the speeding and wild driving tendency as well as driving workload questionnaires. To verify the efficiency of subjective questionnaire information, we tested whether the driver with slow or fast reengagement and stabilization time can be detected based on machine learning techniques and obtained results. We expect to apply these results to training programs for autonomous vehicles' users and personalized human–vehicle interfaces for future autonomous vehicles.     

基于LQR算法的车道保持控制策略

介绍一种基于LQR算法的车道保持控制方法。采用TLC与DLC联合预警模型结合驾驶员意图识别对车辆当前的行驶状态进行判断并在偏离时做出报警,当驾驶员未做出反应时车道保持系统对转向系统的输出转角值进行转向控制,帮助驾驶员纠正车辆偏离动作,在Carsim/Simulink环境下进行仿真,对比不同车速下横/航向偏差量与输出方向盘转角关系,结合车辆偏离预警算法实车测试,对该控制策略的可行性进行验证。结果表明,该控制策略的实时性和鲁棒性较好,具有实用价值。     

Real-Time Non-Driving Behavior Recognition Using Deep Learning-Assisted Triboelectric Sensors in Conditionally Automated Driving

Real-time recognition of non-driving behaviors is of great importance in conditionally automated driving, as it determines the takeover time budget, which in turn has a huge impact on the performance of the takeover. Here, a novel real-time non-driving behavior recognition system (RNBRS) integrating self-powered, low-cost, easy-to-manufacture triboelectric sensors, and a deep learning model is proposed. The structure, working mechanism, and electrical characteristics of triboelectric sensors are investigated and analyzed. Through the ingenious structural design of single-electrode triboelectric sensors and driving simulation experiments under conditional automated driving, non-driving behaviors are captured in the form of electrical signals. A well-trained long short-term memory network model is adopted to recognize the five most typical non-driving behaviors, including phone, console touchpad, driving, monitoring driving, and no operation, and test accuracy of 93.5% is achieved. Demonstration of a set of controlled experiments shows that RNBRS enables vehicles with conditional automation to dynamically adjust takeover time budget based on driver behavior, therefore significantly improving both safety and stability of takeover. This study opens new frontiers for the development of self-powered electronics and inspires new thoughts on human-machine interaction and the safety of autonomous vehicles.     

基于UI设计的交互性与界面视觉设计研究

经济与社会的快速发展最大限度推动了我国的科技发展进程,人们对软件使用与UI设计的要求也在不断地提升。在此情况之下,相关人员必须要对UI设计进行全面的研究分析,以市场和用户需求为导向,对界面视觉设计和交互性设计进行系统的研究与革新,以确保UI设计能够取得预期的成效。文章首先分析了UI设计及其发展现状,然后分析了UI设计当中的交互性功能及其应用,最后分析了UI设计过程当中的界面视觉要素及其设计应用,希望能对相关的研究有所助益。     

Development of advanced parking assistance system

This paper is the first to apply a multilevel driver assistance system in the development of a system to aid in the parking process. The development of this system is described within the iCAN (intelligent car navigation systems) project framework. A parking assistance system, parking administration system, and employed sensor system are described. The general architecture of a driver assistance system based on path planning and human-machine interface (HMI) modules is proposed. The paper follows describing the parking assistance system development using this architecture. The parking possibility region-based path-planning method proposed for implementing the proposed architecture is described, as is the design of the system's HMI. A prototype of the parking assistance system based on the proposed architecture was constructed. The adopted hardware, software, and implementation solutions in this prototype construction are described. Finally, the results of lane and row parking experiments conducted using the prototype system are shown.     

Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles

This paper deals with the development of Human-Centric Intelligent Driver Assistance Systems. Rear-end collisions account for a large portion of traffic accidents. To help mitigate this problem, predictive braking systems and adaptive cruise control systems have been developed. However, these types of systems usually rely solely on the vehicle and vehicle surround sensors, either ignoring the human component of driving or learning the driver's control behavior using only these sensors. As with all human-computer interfaces, this has the potential to work against the driver, distract the driver further, or even annoy the driver so that the driver ignores or disables the system. It is, therefore, important to directly take the driver's intended actions into account when designing a driver assistance system. By using a probabilistic model for the system, warnings and preventative measures can be constructed based on varying levels of situational severity and driver attentiveness and intent. The research is based upon carefully conducted experimental trials involving a human subjects driving in natural manner and on typical freeways in the USA. The experiments, designed by inputs from cognitive scientist, were conducted in a specially designed instrumented vehicle to record important cues associated with driver's behavior, vehicle state, and vehicle surround in a synchronized manner. Quantitative results and analysis of the experimental trials are presented to show the feasibility and promise of this framework to predict the driver's intent to brake, the need for braking given the current situation, and at what level the driver should be warned     

基于ARM的嵌入式标记机控制系统设计

嵌入式标记机控制系统的开发需要解决通信控制器的驱动和图形界面的设计问题.提出了一种基于S3C2410的气动标记机控制系统结构,在介绍USB OHCI设备驱动程序结构和模板的基础上,设计了嵌入式Linux环境下S3C2410 USB OHCI的驱动程序,并以QT/E作为工具,基于Layout进行了GUI的编码,给出了设计思想.最后,提供了该系统的运行实例.     

基于复杂驾驶环境下面部特征识别技术的防疲劳驾驶系统

本课题阐述基于面部特征识别技术的防疲劳驾驶系统研究、开发.通过后台管理系统进行大数据分析,研究驾驶员疲劳规律、预测驾驶员可能出现的疲劳,方便管理人员有针对性的重点关注"常疲劳"的驾驶员,实现精细化管理,尽早预防、尽量避免疲劳驾驶,为行车安全把好关.     

Voltage‐Mode 1.5 Gbps Interface Circuits for Chip‐to‐Chip Communication

In this paper, interface circuits that are suitable for point‐to‐point interconnection with an over 1 Gbps data rate per pin are proposed. To achieve a successful data transfer rate of multi‐gigabits per‐second between two chips with a point‐to‐point interconnection, the input receiver uses an on‐chip parallel terminator of the pass gate style, while the output driver uses the pullup and pulldown transistors of the diode‐connected style. In addition, the novel dynamic voltage level converter (DVLC) has solved such problems as the access time increase and valid data window reduction. These schemes were adopted on a 64 Mb DDR SRAM with a 1.5 Gbps data rate per pin and fabricated using a 0.10 µm dual gate oxide CMOS technology.     

无人机H.265双路视频传输系统的设计与实现

针对无人机视频传输设备接口兼容能力差、编码速率高的现状,设计了兼容高清多媒体接口(High Definition Multimedia Interface,HDMI)、串行数字接口(Serial Digital Interface,SDI)、复合同步视频广播信号(Composite Video Broadcast Signal,CVBS)三种接口的低码率视频传输系统。系统以Hi3519为核心,采用H. 265的方式编码。在接口兼容方面,通过搭配不同的视频接口芯片,兼容多种视频输入接口;在编码效率方面,同时压缩两路1080p60视频仅需3. 2 Mb/s的压缩码率。相比采用H. 264编码方式、需要视频接口转换器的方案,采用一片主处理器和多种视频接口芯片的方案具有低成本、低功耗、小体积的优点,更适用于无人机。通过搭建无线链路测试了系统的视频传输性能,结果表明,系统连接可靠、易于操作,可以实现实时、稳定的传输。     

基于EOG的安全辅助驾驶系统算法设计与实现

为保证驾驶安全,提高车辆控制系统的智能化水平,实现“手不离盘”操作,设计并实现了一种基于眼电图(EOG)的安全辅助驾驶系统。该系统利用安装在驾驶员眼睛周围的生物电极采集其在观测抬头显示器(HUD, head up display)上提示符时所产生的扫视信号,生成多种车载设备控制命令;对原始多导联EOG信号进行端点检测后,使用了独立分量分析(ICA, independent component analysis)方法进行空域滤波后提取眼动信号特征参数,并结合支持向量机实现了上、左与右扫视动作的识别。实验室环境下对所提算法进行了测试,15位受试者在疲劳与非疲劳状态下的在线平均正确率达到了98.43%与96.0%。实验结果表明,基于ICA多类扫视信号识别算法的安全辅助驾驶系统在眼动信号分析中呈现出了良好的分类性能。