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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >RedEye: Preventing Collisions Caused by Red-Light Running Scooters With Smartphones
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RedEye: Preventing Collisions Caused by Red-Light Running Scooters With Smartphones

机译：RedEye：通过智能手机防止因红灯行驶的踏板车引起的碰撞

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摘要

In this paper, we present a scooter collision avoidance system that can identify red-light runners (RLRs) at intersections. When the RLR behavior is detected, the system would advise the RLR to slow down immediately and warn nearby vehicles on the intersecting road in real time. In particular, we do not consider infrastructure-based solutions such as those utilizing a radar or a camera. This is because, in addition to high implementation costs, collisions can be only avoided at intersections where such infrastructure configurations are deployed. Instead, we advance an on-scooter solution using smartphones carried by scooter riders. Smartphones provide a useful platform that has a high penetration rate, more than sufficient computational power, inertial sensors to reflect the driving behavior, and the communication capability to transmit or receive information from other vehicles. In our system, we utilize a support vector machine and design an RLR classifier for learning and predicting RLR behaviors. The evaluation results show that our system is able to achieve over 70% recognition rates when distinguishing between RLR and non-RLR cases, as compared with approximately 80% recognition rates of the infrastructure-based (and higher cost) solution using a laser range finder (LADAR).
机译：在本文中，我们提出了一种踏板车防撞系统，该系统可以识别十字路口的红灯行驶者（RLR）。当检测到RLR行为时，系统会建议RLR立即减速并实时警告相交道路上的附近车辆。特别是，我们不考虑基于基础设施的解决方案，例如那些利用雷达或摄像机的解决方案。这是因为，除了高昂的实施成本外，只能在部署了此类基础架构配置的交叉路口避免冲突。取而代之的是，我们使用踏板车骑士携带的智能手机来推进踏板摩托车解决方案。智能手机提供了一个有用的平台，该平台具有很高的渗透率，足够的计算能力，惯性传感器以反映驾驶行为，以及从其他车辆发送或接收信息的通信能力。在我们的系统中，我们利用支持向量机并设计了一个RLR分类器，用于学习和预测RLR行为。评估结果表明，在区分RLR和非RLR情况时，我们的系统能够实现70％以上的识别率，而使用激光测距仪的基于基础设施（且成本更高）的解决方案的识别率约为80％ （激光）。

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