Self-supervised Adaptive Weighting for Cooperative Perception in V2V Communications
This work addresses practical limitations in V2V communications for autonomous driving, offering an incremental improvement over existing cooperative fusion models.
The paper tackles the problem of dynamic performance degradation in cooperative perception for V2V communications caused by channel impairments, proposing a self-supervised adaptive weighting model that significantly outperforms benchmarks without weighting, as validated by numerical results and visualization examples.
Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately investigated. In particular, current cooperative fusion models rely on supervised models and do not address dynamic performance degradation caused by arbitrary channel impairments. In this paper, a self-supervised adaptive weighting model is proposed for intermediate fusion to mitigate the adverse effects of channel distortion. The performance of cooperative perception is investigated in different system settings. Rician fading and imperfect channel state information (CSI) are also considered. Numerical results demonstrate that the proposed adaptive weighting algorithm significantly outperforms the benchmarks without weighting. Visualization examples validate that the proposed weighting algorithm can flexibly adapt to various channel conditions. Moreover, the adaptive weighting algorithm demonstrates good generalization to untrained channels and test datasets from different domains.