SEIP: Simulation-based Design and Evaluation of Infrastructure-based Collective Perception

by   Ao Qu, et al.

Infrastructure-based collective perception, which entails the real-time sharing and merging of sensing data from different roadside sensors for object detection, has shown promise in preventing occlusions for traffic safety and efficiency. However, its adoption has been hindered by the lack of guidance for roadside sensor placement and high costs for ex-post evaluation. For infrastructure projects with limited budgets, the ex-ante evaluation for optimizing the configurations and placements of infrastructure sensors is crucial to minimize occlusion risks at a low cost. This paper presents algorithms and simulation tools to support the ex-ante evaluation of the cost-performance tradeoff in infrastructure sensor deployment for collective perception. More specifically, the deployment of infrastructure sensors is framed as an integer programming problem that can be efficiently solved in polynomial time, achieving near-optimal results with the use of certain heuristic algorithms. The solutions provide guidance on deciding sensor locations, installation heights, and configurations to achieve the balance between procurement cost, physical constraints for installation, and sensing coverage. Additionally, we implement the proposed algorithms in a simulation engine. This allows us to evaluate the effectiveness of each sensor deployment solution through the lens of object detection. The application of the proposed methods was illustrated through a case study on traffic monitoring by using infrastructure LiDARs. Preliminary findings indicate that when working with a tight sensing budget, it is possible that the incremental benefit derived from integrating additional low-resolution LiDARs could surpass that of incorporating more high-resolution ones. The results reinforce the necessity of investigating the cost-performance tradeoff.


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