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首页> 外文期刊>Jordan Journal of Mechanical and Industrial Engineering >Monitoring Algorithm for Speed Information of Autonomous Vehicles Based on Magnetoresistive Sensor
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Monitoring Algorithm for Speed Information of Autonomous Vehicles Based on Magnetoresistive Sensor

机译：基于磁阻传感器的自动驾驶汽车速度信息监控算法

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摘要

Based on a magnetoresistive sensor, an algorithm for speed information monitoring of autonomous vehicles suitable for fewer target feature points is proposed. When using a magnetoresistive sensor on the road, the monitoring principle of the magnetoresistive sensor is that the magnetoresistive sensor generates a voltage signal and sends it to a signal processing module. In the signal processing module, the vehicles signal is compared with a threshold value to determine whether the vehicle is present. In the signal processing module, the vehicle signal is compared in the presence of the vehicle, and the node time synchronization technology is used to select the two-node autonomous vehicle speed information monitoring method. Then, the speed information of the autonomous vehicles in motion is monitored in real time. The vehicle speed information is sent to the upper node by using the single chip microcomputer. It is then sent to the coordinator module using ZigBee technology in the wireless sensor network. Finally, it is sent to the intelligent traffic monitoring center to achieve speed information monitoring of autonomous vehicles. The experimental results show that the accuracy rate of the autonomous vehicles’ speed information monitored by the algorithm is above 97%, and the monitored energy consumption is only 13.5 J. This shows that the algorithm’s monitoring accuracy and energy consumption have an advantage.
机译：基于磁阻传感器，提出了一种适用于目标特征点较少的自动驾驶汽车速度信息监控算法。在道路上使用磁阻传感器时，磁阻传感器的监视原理是：磁阻传感器生成电压信号并将其发送到信号处理模块。在信号处理模块中，将车辆信号与阈值进行比较，以确定是否存在车辆。在信号处理模块中，在存在车辆的情况下比较车辆信号，并使用节点时间同步技术选择两节点自主车辆速度信息监视方法。然后，实时监视行驶中的自动驾驶车辆的速度信息。通过使用单片机将车速信息发送到上位节点。然后使用无线传感器网络中的ZigBee技术将其发送到协调器模块。最后，将其发送到智能交通监控中心，以实现自动驾驶汽车的速度信息监控。实验结果表明，该算法监测的自动驾驶汽车速度信息的准确率达到97％以上，监测的能耗仅为13.5J。这表明该算法的监测精度和能耗具有优势。

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