Enhancing Autonomous Vehicles' Situational Awareness With Dynamic Maps: Cooperative Prediction on Edge, Cloud and Vehicle

Autonomous vehicles (AVs) are widely recognized as a pivotal solution for fostering efficiency and safety of future society. Meanwhile, the limitations of on-board sensors, such as physical occlusions and limited field of view (FoV), have sparked significant interest in cooperative perception (CP). However, CP introduces delays in perception and communication tasks, which may hinder AVs' situational awareness. To propel the evolution of CP and further enhance AVs' situational awareness, this paper introduces a novel concept: cooperative prediction (CPd). CPd extends the situational awareness of CP along the temporal axis, overcoming the line-of-sight, FoV, and range-of-time limitations. Leveraging dynamic maps, a CPd framework that seamlessly operates across edge, cloud, and vehicle is established. Edge-based, cloud-based, and vehicle-based CPd modes are proposed to validate the CPd concept. Extensive real-world experiments are carried out to evaluate the performance of our CPd system, comparing it with a conventional CP system. The experimental results show that the CPd system surpasses the CP system by providing accurate zero-lag perception data and reliable prediction data, thereby enhancing AVs' situational awareness in both spatial and temporal domains. Besides, our findings reveal that the cloud-based CPd operates with the highest efficiency, whereas the vehicle-based CPd may encounter fatal failures.