基于矢量传感器的无人驾驶车辆信号远程监测系统

Remote Monitoring System for Unmanned Vehicle Signal Based on Vector Sensor

摘要:

传统无人驾驶车辆远程检测系统，由于信号传输距离的影响，信号波在传输过程在电路耦合率衰减下出现监测信号清晰度降低，信号整体监测效果无法满足实际检测系统应用要求的问题。对此设计系统通过采用矢量传感器对系统变量、模量与定量数据进行矢量统一计算，提出基于矢量传感器的无人驾驶车辆信号远程监测系统。首先完成基于矢量传感器的系统总体框架设计；然后根据设计框架建立无人驾驶车辆监控信号交互硬件单元，通过不同矢量传感器构成监控信号数据矢量转换计算平台；最后通过对硬件平台的矢量化软件适配，完成硬件与软件监控信号统一，提升信号传输强度，增强信号清晰度，从而满足无人驾驶车辆信号远程监测要求，完成提出系统的设计。通过对比实验数据表明，提出设计的基于矢量传感器的无人驾驶车辆信号远程监测系统，能够在50km~100km范围内稳定监测车辆状态，车辆状态数据监测实时性与连续性明显优于传统检测系统。

Abstract:

Due to the influence of signal transmission distance, the traditional remote detection system for driverless vehicles has the problem that the clarity of the monitoring signal is reduced under the attenuation of circuit coupling rate, and the overall monitoring effect of the signal can not meet the application requirements of the actual detection system. In this design system, vector sensors are used to calculate the system variables, modulus and quantitative data, and a remote signal monitoring system for driverless vehicles based on vector sensors is proposed. Firstly, the overall framework design of the system based on vector sensor is completed; secondly, according to the design framework, the interactive hardware unit of unmanned vehicle monitoring signal is established, and the vector conversion calculation platform of monitoring signal data is constituted by different vector sensors; finally, through the vector quantization software adaptation of the hardware platform, the unification of hardware and software monitoring signal is completed, the signal transmission strength is improved, and the efficiency is improved In order to meet the requirements of remote signal monitoring for driverless vehicles, the design of the proposed system is completed. By comparing the experimental data, it shows that the remote monitoring system of driverless vehicle signal based on vector sensor can stably monitor the vehicle status in the range of 50km ~ 100km, and the real-time and continuity of vehicle status data monitoring is obviously better than the traditional detection system.