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This paper presents a steering control method for lane-following in a vehicle using an image sensor. With each image frame
acquired from the sensor, the steering control method determines target position and direction, and constructs a travel path
from the current position to the target position either as an Arc-path or S-path. The steering angle is calculated from the
travel path thus generated, and the vehicle follows the travel path via motor-control. The method was tested using a vehicle
dubbed as KAV (Korea Autonomous Vehicle) along an expressway (Seoul Inner Beltway) trajectory with a variety of radii (50
m ∼ 300 m) while traveling at a speed of 60 km/h to 80 km/h. Compared with an experienced human driver, the method showed
little much difference in performance in terms of lane-center deviation. The proposed method is currently employed for high
speed autonomous driving as well as for stop and go traffic. 相似文献
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本文介绍一种船载多目标水声跟踪系统,它以多片TMS320C25DSP为核心采用并行处理技术,以达到对下快速目标实时准确定位的目的。 相似文献
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水下运动物体在相互接近过程中的水动力计算 总被引:6,自引:1,他引:5
用面元法计算了两个水下物体在相互接近过程中对水动力的影响,给出了水动力系统系数随物体间距离的变化曲线。该计算程序经对圆球的计算值与试验值比较了得到了验证。本研究对水下双潜器的近距离协同作业和潜器在接近障碍物过程中的运动控制能够提供较好的数学模型。 相似文献
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The convergence of information and communication technologies (ICT) with automotive technologies has already resulted in automation features in road vehicles and this trend is expected to continue in the future owing to consumer demand, dropping costs of components, and improved reliability. While the automation features that have taken place so far are mainly in the form of information and driver warning technologies (classified as level I pre-2010), future developments in the medium term (level II 2010–2025) are expected to exhibit connected cognitive vehicle features and encompass increasing degree of automation in the form of advanced driver assistance systems. Although autonomous vehicles have been developed for research purposes and are being tested in controlled driving missions, the autonomous driving case is only a long term (level III 2025 +) scenario. This paper contributes knowledge on technological forecasts regarding automation, policy challenges for each level of technology development and application context, and the essential instrument of cost-effectiveness for policy analysis which enables policy decisions on the automation systems to be assessed in a consistent and balanced manner. The cost of a system per vehicle is viewed against its effectiveness in meeting policy objectives of improving safety, efficiency, mobility, convenience and reducing environmental effects. Example applications are provided that illustrate the contribution of the methodology in providing information for supporting policy decisions. Given the uncertainties in system costs as well as effectiveness, the tool for assessing policies for future generation features probabilistic and utility-theoretic analysis capability. The policy issues defined and the assessment framework enable the resolution of policy challenges while allowing worthy innovative automation in driving to enhance future road transportation. 相似文献
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