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首页> 外文期刊>Intelligent Transportation Systems Magazine, IEEE >Parking Assistance System for Leaving Perpendicular Parking Lots: Experiments in Daytime/Nighttime Conditions
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Parking Assistance System for Leaving Perpendicular Parking Lots: Experiments in Daytime/Nighttime Conditions

机译:离开垂直停车场的停车辅助系统:白天/夜间条件下的实验

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Abstract-Backing-out and heading-out maneuvers in perpendicular or angle parking lots are one of the most dangerous maneuvers, specially in cases where side parked cars block the driver view of the potential traffic flow. In this paper a new vision-based Advanced Driver Assistance System (ADAS) is proposed to automatically warn the driver in such scenarios. A monocular grayscale camera was installed at the back-right side of a vehicle. A Finite State Machine (FSM) defined according to three CANBus variables and a manual signal provided by the user is used to handle the activation/deactivation of the detection module. The proposed oncoming traffic detection module computes spatiotemporal images from a set of pre-defined scan-lines which are related to the position of the road. A novel spatio-temporal motion descriptor is proposed (STHOL) accounting for the number of lines, their orientation and length of the spatio-temporal images. Some parameters of the proposed descriptor are adapted for nighttime conditions. A Bayesian framework is then used to trigger the warning signal using multivariate normal density functions. Experiments are conducted on image data captured from a vehicle parked at different locations of an urban environment, including both daytime and nighttime lighting conditions. We demonstrate that the proposed approach provides robust results maintaining processing rates close to real time.
机译:垂直或倾斜停车场的摘要撤退和驶出演习是最危险的演习之一,尤其是在侧面停车的汽车挡住驾驶员对潜在交通流的视野的情况下。本文提出了一种新的基于视觉的高级驾驶员辅助系统(ADAS),可以在这种情况下自动警告驾驶员。单眼灰度相机安装在车辆的右后侧。根据三个CANBus变量和用户提供的手动信号定义的有限状态机(FSM)用于处理检测模块的激活/停用。拟议的即将到来的交通检测模块从一组与道路位置相关的预定义扫描线计算时空图像。提出了一种新颖的时空运动描述符(STHOL),该描述符考虑了线的数量,其方向和时空图像的长度。提出的描述符的某些参数适用于夜间条件。然后使用贝叶斯框架使用多元正态密度函数触发警告信号。对从停在城市环境中不同位置的车辆捕获的图像数据进行了实验,包括白天和夜间的照明条件。我们证明了所提出的方法可提供稳定的结果,保持接近实时的处理速度。

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