This study addresses the state estimation problem for the preceding vehicle in vehicle-to-vehicle (V2V) cooperative perception, with the objective of balancing estimation accuracy against communication load under constraints of limited bandwidth and non-Gaussian noise interference. To this end, an extended Kalman filtering algorithm is proposed that integrates an event-triggered (ET) mechanism with the maximum correntropy criterion (MCC). The event-triggered mechanism adaptively regulates communication frequency, significantly reducing network load while preserving estimation performance. Meanwhile, the maximum correntropy criterion enhances the filter’s robustness against non-Gaussian noise, enabling high-precision state estimation under limited communication resources. Simulation results under double lane-change and continuous sinusoidal steering scenarios show that, compared to conventional extended Kalman filtering and existing event-triggered robust filtering methods, the proposed algorithm achieves better estimation accuracy and stability in non-Gaussian noise environments. Moreover, it reduces the average communication load by approximately 68% without compromising estimation performance. This work provides an effective solution for state estimation in V2V cooperative perception that combines high robustness with high communication efficiency.
Ren et al. (Wed,) studied this question.