An Enhanced 3D Sensor Deployment Method for Intelligent Cooperative Sensing in Connected and Autonomous Vehicles

Currently, heterogeneous driving scenarios and complex traffic conditions challenge connected and autonomous vehicles (CAVs) to achieve accurate sensing of road conditions. Existing research on the sensing capabilities of vehicles only relys on adding more onboard sensors, which makes the driving safety unable to be guaranteed due to the installment and cost limit of various sensors. Therefore, this paper proposes an enhanced 3D sensor deployment method to break through the sensing capabilities of CAVs’ own equipment limitations. By efficiently utilizing road infrastructure, reasonable roadside sensor deployment will effectively assist CAVs to expand the sensing range and improve overall sensing accuracy. Firstly, in order to address the limitations of existing works that often rely on simplified sensor models and idealized road conditions, we propose a Bresenham-based sensor and environment model that can be used to construct realistic road environments. Secondly, a decision transformer (DT)-based method is adopted to solve the problem of optimal deployment of sensors in road environments. Our approach effectively addresses the limitations of traditional static deployment methods, which often fail to consider the complexities of real-world driving conditions and the diverse factors influencing optimal sensor deployment. Finally, in order to solve the problem of DT delayed rewards, we propose a two-layer optimization method to redistribute the reward function to solve the challenge of local optimization. A large number of simulations oriented to sensing effects not only verify the effectiveness of the sensor deployment method but also ensure the reliability of sensing assistance.