Split Covariance Intersection Filter based Front-Vehicle Track Estimation for Vehicle Platooning without Communication

Vehicle platooning is an innovative technology for intelligent transportation systems. Each vehicle in the platoon is required to autonomously follow its front vehicle's path unconditionally and accurately. For platoons without communication, accurate path and velocity estimation of the front vehicle is challenging and crucial. Instead of memorizing the original detection result of the front vehicle to generate a path, the path is estimated by fusing motion prediction and observation in this paper. Since there is a correlation between the two estimates, Split Covariance Intersection Filter is used to guarantee the fusion consistency. Besides, a motion model considering velocity is used in the filter to achieve a precise estimate of the predecessor's velocity simultaneously. Moreover, a path generation approach is designed in a high-frequency loop independent of front vehicle detection to improve continuity of the path. Experimental validations in the real-world environment highlight the remarkable improvement in the accuracy of both path and velocity estimation. Meanwhile, the path generated by the proposed approach is more continuous compared with the commonly used method. Furthermore, complete vehicle platooning demonstrations in diverse environments prove the practicality and robustness of the proposed approach.