A Study of Optimization Strategies of Autonomous Vehicle Related Technology

This paper studies the optimization strategies of the related technologies used in autonomous vehicles, including perception fusion and control algorithm. With the rapid development of related technologies of autonomous vehicles, perception fusion and control algorithm have become the core technology of autonomous vehicles. In this study, firstly, a theoretical model of perception fusion was established for the hardware modules of radar and vision in the perception fusion technology of autonomous vehicle, and a comparative analysis was made for different application scenarios of various radar and vision modules, and an optimization strategy of perception fusion is proposed. Secondly, aimed at the common control algorithms of autonomous vehicles, including PID control, model predictive control (MPC) and sliding mode control (SMC), this study gave the optimization strategy of the control algorithm by comparing the performance of their time complexity and space complexity. Finally, this study drew a conclusion: In the aspect of perception fusion, improving the accuracy of radar detection and vision module from the hardware point of view and improving the signal-to-noise ratio from the software point of view and strengthening the anti-jamming ability can effectively improve the performance of perception fusion. In the aspect of control algorithm, the control performance can be effectively improved by improving the time complexity and space complexity of the algorithm itself.