Enhanced 3D Sensor Deployment Method for Cooperative Sensing in Connected and Autonomous Vehicles
Pincan Zhao, Changle Li, Fei Richard Yu, Yuchuan Fu · 2023
Connected and Autonomous Vehicles (CAVs) are emerging as an inevitable trend in the future of the automotive industry, drawing collaborative attention from academia, industry, and government sectors. However, the inherent limitations in the sensing capabilities of CAVs restrict their safety and reliability while navigating complex road environment, confining them to address only a narrow scope of potential issues. Enhancing vehicle sensing capabilities by strategically deploying sensors along the road for cooperative sensing can effectively extend the range of vehicle sensing, achieving beyond-line-of-sight, high-precision sensing. It's foreseeable that an increased number and accuracy of roadside sensors would provide more advanced assistance to vehicles. Yet, due to deployment cost constraints, a high-density sensor deployment remains impractical. The challenge of optimizing sensor coordination within a limited budget still prevails. In this paper, contrasting with prior simplistic sensor setups and ideal environmental factors from previous works, we first remodel sensors and networks in light of the intricate real-world environment. By considering the spatial model of actual conditions, the 3D sensor network deployment issue is redefined. Furthermore, we introduce an optimization approach for 3D sensor deployment using the decision transformer, circumventing issues related to numerical reward modeling and disparities in policy learning and deployment data. Extensive simulation results attest that our proposed method effectively and efficiently enhances vehicle sensing capabilities within a limited cost.