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Outline
To address the limited generalization ability of rule-based methods in long-tail scenarios, as well as the overfitting issue and poor interpretability of data-driven models in autonomous driving, this paper proposes an autonomous driving framework, based on a Large Language Model (LLM) and Deep Deterministic Policy Gradient (DDPG). The framework integrates commonsense knowledge with environmental information to generate interpretable driving decisions, while the DDPG algorithm is employed to achieve low-level continuous control. In the memory module, a hybrid retrieval mechanism that combines numerical-feature similarity and semantic-feature similarity is designed to match similar historical cases in real time, thereby supporting dynamic decision-making. Experimental results show that compared with Rule-Driving and DQN-Driving, the proposed method achieves relative improvements of approximately 33% and 14% in success rate, respectively, in heterogeneous test Scenario A. In heterogeneous test Scenario B, the relative improvements further increase to approximately 70% and 42%, demonstrating stronger cross-scenario generalization capability and environmental adaptability.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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