Core Concepts
Developing an accuracy-aware cooperative sensing and computing scheme for connected autonomous vehicles.
Abstract
The article proposes a scheme for accurate perception among connected autonomous vehicles (CAVs) by enabling fine-grained raw-level cooperative sensing and computing. It focuses on accuracy-aware data selection, joint subtask placement, and resource allocation to minimize resource costs while meeting delay and accuracy requirements. The proposed solution utilizes supervised learning models, optimization algorithms, and simulation results to demonstrate improved accuracy awareness and resource efficiency compared to benchmark solutions.
- Introduction to autonomous driving's foundation on CAVs' environment perception.
- Challenges of relying solely on onboard sensors for complete environment perception.
- Proposal of a cooperative sensing scheme leveraging V2X communication technologies.
- Trade-offs between resource efficiency and performance enhancement in cooperative sensing levels.
- Importance of scalable raw-level cooperative sensing for high perception performance.
- Development of an accuracy-aware cooperative sensing and computing scheme.
- Utilization of supervised learning models for object classification accuracy estimation.
- Formulation of an optimization problem for joint data selection, subtask placement, and resource allocation.
- Proposal of a genetic algorithm-based iterative solution for optimization problem resolution.
Stats
Simulation results demonstrate the accuracy awareness achieved by the proposed scheme.