Path planning is one of the most crucial elements of autonomous driving (AD). Due to its capacity to directly make judgments based on observation and learn from the environment, learning-based path planning techniques are of interest to many academics. The standard reinforcement learning approach of the deep Q-network has made major strides in AD since the agent normally learns driving tactics simply by the intended reward function, which is difficult to adapt to the driving scenarios of urban roadways. However, such methodologies rarely use the global path data to address the problem of directional planning, like turning around at an intersection. In addition, the link between different motion instructions like these might easily lead to an erroneous prediction of the route orders due to the fact that the steering and the accelerator are independently governed in a real-world driving system. This research proposes and implements a Provisional Cross-layered Deep Q-Network (PC-DQN) for path planning in end-to-end autonomous vehicles, where the universal path is employed to direct the vehicles from the starting point to ending point. We employ the concept of Improved Harmony Search optimized fuzzy control (HIS-FC) and propose a defuzzification approach to increase the stability of anticipating the values of various path instructions in order to manage the reliance of distinct path instructions in Q-networks. We carry out extensive tests in the CARLA simulator and contrast our approach with cutting-edge approaches. The suggested strategy outperforms existing methods in terms of learning efficiency and driving reliability, according to experimental findings.