基于云和高斯过程的网联车辆协同式道路参数估计

Cloud-Based Collaborative Road Profile Estimation Using Gaussian Process

摘要:

近年来智能网联汽车发展迅速，云端预先存储的道路参数信息对于提升网联汽车的悬架控制以及检测路面不规则度至关重要。目前关于道路参数估计的工作大多在单个车辆上完成，此类算法对于车辆模型不确定性以及测量误差较敏感。针对该问题，提出了一种新的协同式估计架构，该架构能够充分利用多个同质的网联汽车的测量信息以提高估计精度。首先，在云端利用前方行驶的全部车辆的数据对高斯过程模型进行训练以通过众包方式获取道路参数的估计结果。然后，该结果以未测量的方式发送到后方相邻车辆，后方单个车辆结合自车车载传感器（如加速度计、横摆角速度以及侧倾角速度）和由云端获取的基于众包高斯过程估计结果，使用卡尔曼滤波对该估计结果进一步优化。进而估计结果被发送到云端以更新高斯过程模型。大量的仿真实验结果表明，以该种方式使用云端估计的道路参数作为额外的未测量信息能够提高道路参数的估计精度，验证了该算法的有效性。

Abstract:

Road profile information is used to improve vehicle suspension control and detect road irregularities such as potholes. While a great many road profile estimation approaches exist， they have been traditionally performed in a single-vehicle setting， which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations， this paper presents a new collaborative estimation framework that exploits multiple heterogeneous vehicles to iteratively improve the estimation. Specifically， each vehicle combines its onboard measurements （e.g.， accelerometers and yaw/roll rate sensors） with a crowdsourced Gaussian process （GP） estimation from the cloud into a Kalman filter （KF） to iteratively refine the estimation. The GP is trained from the crowdsourced local estimations of all prior participating vehicles， which is then sent to the latest participating vehicle as “pseudo-measurements” to enhance the onboard estimation. The resultant local onboard estimation is sent back to the cloud to update the GP. It is shown that using the GP as additional pseudo-measurements can iteratively improve the road profile estimation performance from vehicle by vehicle. Extensive simulations are performed to show the efficacy of the proposed approach.