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.

翻译：自动驾驶（AD）技术依托人工智能，致力于实现车辆自动化。为简化传统驾驶系统而出现的端到端策略，通过整合感知、决策与控制环节，为高级驾驶功能开辟了新途径。尽管潜力巨大，当前仍面临数据效率、训练复杂性与泛化能力不足等挑战。本研究针对上述问题提出一种新型端到端自动驾驶训练模型，旨在增强系统适应性与智能水平。该模型在策略网络中引入Transformer模块，首先通过行为克隆（BC）预训练获取更新梯度，随后采用人类引导的强化学习（RLHG）进行微调，使模型适配特定驾驶环境，力图突破模仿学习（IL）的性能瓶颈。微调过程融入人类交互，通过监督、干预、示范与奖励反馈引导模型习得更高效安全的驾驶行为。仿真结果表明，该框架能加速学习进程，实现精准控制并显著提升安全可靠性。相较于其他先进基线方法，本方案在挑战性驾驶任务中表现卓越。Transformer模块与人类引导微调的引入为自动驾驶领域的研究与应用提供了有价值的思路与方法。