基于网联车辆数据融合的路面平整度评估方法

Road Roughness Assessment Based on Fusion of Connected-vehicles Data

智能网联汽车的快速发展为道路基础设施服役性能的监测提供了更加便捷、高效的全新解决方案。为发挥智能网联车辆优势,提高车载响应类路面平整度检测的便捷性和准确性,首先提出了一种基于车辆振动响应信号的路面纵断面高程反演及平整度估计方法,该方法通过对车辆垂向动力学的合理建模及模型参数辨识,以车身振动响应信号作为测量值,借助增广卡尔曼滤波算法,对路面断面高程进行反演估计,进而求解国际平整度指数。随后,采用高斯过程模型对多车估计结果进行拟合以实现多车结果的融合解析。考虑到智能网联汽车可实现车车互联通信的优势,进一步将前车融合结果纳入当前车辆估计算法,并将其作为伪测量量,实现多车协同估计以提高当前车辆估计结果的准确性。借助数值仿真及实测数据对前述方法进行验证,实测结果表明:所提出的单车估计方法所得国际平整度指数的估计值与实测值相关性可达87%,均方根误差约为0.26 m·km-1;多部车辆的重复性测试验证了该方法的准确性和可重复性;多车结果融合及协同估计策略不仅可降低单车估计结果的误差,且可在一定程度上提高单车估计算法的稳定性。在算法验证基础上,设计了一套可实现路面平整度在线评估的原型系统,并提出了智能网联环境下路面平整度评估框架,进而对该系统及框架进行了离线验证。基于该框架,不仅可实现路面平整度的单车在线估计,还可对多车感知结果进行融合,并将平台端融合结果用于终端计算中,实现云-端协同估计,从而提高终端反演的准确性和鲁棒性。

The rapid development of intelligent connected vehicles(ICV)provides a new solution for monitoring infrastructure performance in a more convenient and scalable manner.To improve the efficiency and accuracy of the vehicle dynamic-response-based pavement roughness evaluation method,this study proposes a pavement profile inversion method based on an augmented Kalman filter using vehicle vibration response signals as the input.With the estimated pavement profile,the international roughness index(IRI)can then be calculated to evaluate the roughness of the pavement.Subsequently,a Gaussian process model was used to fit the estimated profile results from multiple vehicles to achieve a fusion resolution of the multi-vehicle results.With the help of the vehicle communication of intelligent connected vehicles,the fused results of previous vehicles can be incorporated as pseudo-measurements in an ego vehicle.Thus,the pavement profile estimation accuracy and robustness of the ego vehicle can be further improved.Numerical simulations and field tests are performed to validate the proposed method.The test results indicated that the proposed estimation method can provide good accuracy for IRI prediction.The correlation between predicted and measured IRI values was approximately 87%.The root mean square error of the predicted IRI value was approximately 0.26 m·km-1.Repeatability tests with multiple vehicles verified the accuracy and repeatability of this method.Moreover,the multi-vehicle result fusion and collaborative estimation strategy can not only reduce the error of the single-vehicle estimation result but also improve the stability of the single-vehicle estimation algorithm.Based on the algorithm validation,a prototype device for online pavement roughness evaluation was designed,and a framework for pavement roughness evaluation in an ICV environment was proposed and validated offline.Based on this framework,not only can the single-vehicle online estimation of pavement roughness be realized,but multivehicle estimation results can also be aggregated.The most practical appeal of this framework is that the fused results at the platform side can be further used in edge calculations to realize cloud-edge collaborative estimation and improve the accuracy and robustness of edge inversion.