  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.
