  Autonomous driving (AD) technology, leveraging artificial intelligence,
strives for vehicle automation. End-toend strategies, emerging to simplify
traditional driving systems by integrating perception, decision-making, and
control, offer new avenues for advanced driving functionalities. Despite their
potential, current challenges include data efficiency, training complexities,
and poor generalization. This study addresses these issues with a novel
end-to-end AD training model, enhancing system adaptability and intelligence.
The model incorporates a Transformer module into the policy network, undergoing
initial behavior cloning (BC) pre-training for update gradients. Subsequently,
fine-tuning through reinforcement learning with human guidance (RLHG) adapts
the model to specific driving environments, aiming to surpass the performance
limits of imitation learning (IL). The fine-tuning process involves human
interactions, guiding the model to acquire more efficient and safer driving
behaviors through supervision, intervention, demonstration, and reward
feedback. Simulation results demonstrate that this framework accelerates
learning, achieving precise control and significantly enhancing safety and
reliability. Compared to other advanced baseline methods, the proposed approach
excels in challenging AD tasks. The introduction of the Transformer module and
human-guided fine-tuning provides valuable insights and methods for research
and applications in the AD field.
