ACP-based Parallel Multi-Sensor Optimization Configuration System for Intelligent Vehicles

Perception technology plays a significant role in ensuring driving safety and other aspects. However, achieving the optimal configuration of multi-source sensors is a complex problem when designing an intelligent vehicle perception system. In this paper, the ACP method (artificial system, computational experiments, and parallel execution) is introduced to address the problem of optimally configuring multi-source sensors in intelligent vehicles. Firstly, an artificial system is constructed to simulate the effects of different scenarios on the performance of sensing sensors. Secondly, the configuration method for different sensing sensors, under the condition of maximum coverage of the sensing area, is determined through computational experiments. This includes determining the number of installations, positions, and directions. Finally, dynamic optimization of the configuration scheme is achieved through parallel execution, resulting in an optimal configuration scheme of multi-source sensors for intelligent vehicles that meets the requirements of different scenarios. Additionally, this paper generates a multi-sensor configuration scheme through computational experiments based on a hypothetical car model. The effectiveness of the parallel optimal configuration method proposed in this paper is verified by evaluating the coverage of the perception area under various scenarios.