With the advancement of data-driven techniques, addressing continuous con-trol challenges has become more efficient. However, the reliance of these methods on historical data introduces the potential for unexpected decisions in novel scenarios. To enhance performance in autonomous driving and collision avoidance, we propose a symbiotic fusion of policy gradient with safety-based control. In this study, we em-ploy the Deep Deterministic Policy Gradient (DDPG) algorithm to enable autono-mous driving in the absence of surrounding vehicles. By training the vehicle's driving policy within a stable and familiar environment, a robust and efficient learning pro-cess is achieved. Subsequently, an artificial potential field approach is utilized to formulate a collision avoidance algorithm, accounting for the presence of surround-ing vehicles. Furthermore, meticulous consideration is given to path tracking meth-ods. The amalgamation of these approaches demonstrates substantial performance across diverse scenarios, underscoring its potential for advancing autonomous driving while upholding safety standards.

翻译：随着数据驱动技术的发展，连续控制问题的求解效率显著提升。然而，这些方法对历史数据的依赖可能导致在未知场景中产生意外决策。为提升自动驾驶与碰撞规避性能，我们提出一种策略梯度与安全控制的共生融合方案。本研究采用深度确定性策略梯度（DDPG）算法实现无周围车辆情况下的自动驾驶。通过在稳定且熟悉的环境中训练车辆驾驶策略，获得了稳健高效的学习过程。随后，利用人工势场法构建考虑周围车辆存在的碰撞规避算法。此外，还细致考虑了路径跟踪方法。这些方法的融合在多样化场景中展现出优异性能，凸显了其在保障安全标准前提下推动自动驾驶技术发展的潜力。