Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenarios and causal relationships, hindering adaptability and interpretability in varied environments. To address the above problems, we introduce LeapAD, a novel paradigm for autonomous driving inspired by the human cognitive process. Specifically, LeapAD emulates human attention by selecting critical objects relevant to driving decisions, simplifying environmental interpretation, and mitigating decision-making complexities. Additionally, LeapAD incorporates an innovative dual-process decision-making module, which consists of an Analytic Process (System-II) for thorough analysis and reasoning, along with a Heuristic Process (System-I) for swift and empirical processing. The Analytic Process leverages its logical reasoning to accumulate linguistic driving experience, which is then transferred to the Heuristic Process by supervised fine-tuning. Through reflection mechanisms and a growing memory bank, LeapAD continuously improves itself from past mistakes in a closed-loop environment. Closed-loop testing in CARLA shows that LeapAD outperforms all methods relying solely on camera input, requiring 1-2 orders of magnitude less labeled data. Experiments also demonstrate that as the memory bank expands, the Heuristic Process with only 1.8B parameters can inherit the knowledge from a GPT-4 powered Analytic Process and achieve continuous performance improvement. Code will be released at https://github.com/PJLab-ADG/LeapAD.

翻译：自动驾驶技术因传感器、机器学习及人工智能的进步而取得显著发展。然而，现有方法在处理复杂场景与因果关系方面仍存在困难，制约了其在多变环境中的适应性与可解释性。为解决上述问题，我们提出LeapAD——一种受人类认知过程启发的新型自动驾驶范式。具体而言，LeapAD通过选择与驾驶决策相关的关键对象来模拟人类注意力机制，从而简化环境解析并降低决策复杂度。此外，LeapAD引入创新的双过程决策模块，该模块包含用于深入分析与推理的分析过程（系统-II），以及用于快速经验处理的启发式过程（系统-I）。分析过程凭借其逻辑推理能力积累语言化驾驶经验，并通过监督微调将知识迁移至启发式过程。借助反思机制与持续扩展的记忆库，LeapAD能在闭环环境中从历史错误中持续自我改进。在CARLA中的闭环测试表明，LeapAD在仅依赖摄像头输入的方法中表现最优，且所需标注数据量降低1-2个数量级。实验还证明，随着记忆库的扩展，仅含18亿参数的启发式过程能够继承由GPT-4驱动的分析过程的知识，并实现持续的性能提升。代码将在https://github.com/PJLab-ADG/LeapAD发布。