Cooperative Perception of Autonomous Vehicles for Semi-Connected Environment

Abstract

Connected Autonomous Vehicles (CAV) holds great promise for improving road safety by reducing human error and lowering accident rates. However, the contemporary CAV industry requires implementing cooperative perception systems as proactive means to detect potential accidents and enable CAVs to optimize their future trajectories and decisions. Despite numerous studies, significant challenges persist due to limited communication resources and the absence of essential safety features in many CAV systems. This research addresses these challenges by developing accident-aware cooperative perception systems capable of thriving in resource-constrained network environments. To achieve this objective, three innovative solutions are presented. Firstly, a methodology for optimizing the selection of critical information within limited cellular network resources is introduced. This approach operates at the CAV’s end, where information is selected based on its significance to specific receivers, and at the base-station, which must deal with resource limitations and high message volumes. Remarkably, the base-station transmits more than 95% of message value while discarding 65% to 95% of messages in various experiments with varying available resource blocks. Additionally, this thesis proposes a Game Theory-Based Transmission algorithm (GTBT) to mitigate redundancy, offering the advantage of decentralized redundancy mitigation implementation without additional communication costs. Secondly, this research introduces two adaptive information clustering techniques, specifically Spatial safety-aware Clustering (SC) and Temporal Clustering (TC), which optimize safety-related information by adapting standard generation rules. These techniques significantly enhance safety relevance by up to 12.5X, while simultaneously reducing communication payload by up to 41% across different environments. Furthermore, this research shows that the proposed SC approach can extend existing methods, further reducing payload by more than 20%. Thirdly, this research presents a theoretical benchmark solution, named Holistic COoperative Perception solution (H-COP), a versatile tool that can guide industry leaders, researchers, and policymakers toward safer and more effective CAV systems. As a novel reference point, this study demonstrates the usefulness of H-COP for evaluating various methods and exposing additional limitations in existing standards, specifically, the high number of replicas beyond the maximum allowable senders. Notably, STC effectively eliminates these unnecessary messages, a task that state-of-the-art techniques struggle to accomplish.