基于语言推理和认知记忆的自动驾驶决策模型

doi:10.13229/j.cnki.jdxbgxb.20240606

Abstract

Abstract:

To address the issues of insufficent safety performance and low learning inefficient in traditional autonomous driving systems， an autonomous driving safety decision-making model capable of continuous learning and understanding linguistic information was proposed. Referring to the reasoning decision-making and experience accumulation processes in human driving， this model leverages a large language model （LLM） as the decision-making agent， integrating chain-of-thought reasoning， a two-stage attention mechanism， and cognitive memory storage and retrieval into the contextual safety learning of the driving process. Meanwhile， a kinematic module is employed to convert LLM decisions into executable driving commands， enabling the continuous learning of safe driving experiences. Experimental results demonstrate that the proposed decision-making model significantly improves safety and efficiency compared to rule， reinforcement learning， and knowledge-based approaches， and possesses the capability of continuous learning and adapting driving behaviors based on human instructions， providing a reference for human-like autonomous driving.

Key words: vehicle engineering, autonomous driving, continuous learning, large language model, chain-of-thought reasoning, two-stage attention mechanism

CLC Number:

U495

Xiang WANG,Guo-zhen TAN,Yan-fei PENG,Hao REN,Jian-ping LI. Autonomous driving decision⁃making model based on language reasoning and cognitive memory[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 3918-3927.

Figures/Tables 14

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Table 5

Driving behavior with different commands"

指导命令	$a ˉ$ /（m·s^-2）	$s ˉ$ /rad	$v ˉ$ /（m·s^-1）	$g ˉ$ /m	$l ˉ$
DMA	3.50	0.03	34.7	8.6	7
DMC	0.21	0.01	21.2	41.8	1
NEC	1.87	0.02	28.3	25.6	2

Table 5

Fig.9

References 15

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数据流	参数	类型	说明
输入	model	string	llama2-7b-chat-v2
	messages	list	输入的内容
	result_format	string	用户返回的内容类型
输出	request_id	string	llama2-7b-chat-v2
	output	list	调用结果信息
	usage.input_ tokens	int	用户输入文本转换为Token后的长度
	usage.output_ tokens	int	模型生成回复转换为Token后的长度

结构	取值
输入	［·，6］
层数	编码器［64，64］；2个注意力头；d_k =32；解码器［64，64］
参数量	3.4×10⁴

场景	方法	C	ζ	t	P_D	P_d	C_n
高速环境	LRCMM	0	12.8	26.4	1.35	1.31	13.89
	RBM	4	26.8	19.7	3.25	3.13	27.83
	MFRLM	8	35.2	15.7	3.84	3.45	46.95
	KDM	3	14.6	29.5	2.47	2.31	20.84
十字路口	LRCMM	0	32.8	47.2	1.84	0.91	24.22
	RBM	2	75.1	32.1	4.21	2.64	48.11
	MFRLM	9	68.4	30.5	5.28	1.06	50.50
	KDM	7	42.5	58.5	3.98	3.61	40.31

场景	TAM	CM	C	ζ	t	P_D	P_d	C_n
高速环境	×	×	6	69.5	34.3	4.35	3.94	48.00
	√	×	4	64.7	36.5	3.84	3.12	43.82
	×	√	3	38.6	28.4	2.37	1.26	26.90
	√	√	0	14.7	27.1	1.44	1.29	14.97
十字路口	×	×	2	33.7	48.9	4.21	3.95	38.03
	√	×	3	32.5	47.8	4.18	3.84	37.35
	×	√	1	32.1	47.3	3.89	3.52	35.57
	√	√	0	31.7	46.2	3.74	3.43	34.55

Autonomous driving decision⁃making model based on language reasoning and cognitive memory

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