基于CARLA仿真的端到端自动驾驶算法综述OA

With recent advances in deep learning and hardware computing power,end-to-end autonomous driving models have attracted significant attention.The core idea is to use neural networks to directly map sensory inputs to vehicle control commands,optimizing the entire driving process as a single task.However,testing autonomous driving models in the real world involves substantial costs in terms of manpower and resources,along with considerable safety risks-particularly covering rare or extreme scenarios,such as sudden pedestrian crossings.As a result,it has become a common practice to train models in simulators and subsequently transfer them to real-world applications.In this context,this paper first surveys the major autonomous driving simulators currently in use and then focuses on the development of end-to-end driving algorithms based on the CARLA simulation platform.Existing algorithms are categorized according to their level of reliance on simulation data,and we review their strengths and differences in terms of model input and output design,data augmentation techniques,and training strategy optimization,helping to clarify their interrelationships.We further discuss simulation scene synthesis methods based on game engines and neural radiance fields.Finally,we analyze the advantages and limitations of current approaches and highlight potential future research directions.