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To address the limitations of traditional lane-keeping methods that rely on high-fidelity models and significant computational burden, this paper proposes a lane-keeping assist strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed system integrates vehicle dynamics models with a Deep Reinforcement Learning (DRL) framework. Furthermore, a specialized reward function considering both task completion and lateral error is designed to optimize training convergence speed and tracking accuracy, thereby achieving more precise and efficient performance across various curve scenarios. Simulation experiments demonstrate that the proposed strategy yields significantly smaller lateral errors compared to the traditional Model Predictive Control (MPC) method. Specifically, the Mean Absolute Lateral Error (MALE) is reduced by 42%~72% compared to MPC, highlighting the potential of DRL in lane-keeping tasks.

, authors=Xinguang Yang^{1, 2}, Konghui Guo¹, authorsList=Xinguang Yang, Konghui Guo, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308463017809695600, articleId=1308463017415431022, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于深度强化学习的车道保持策略研究, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对传统车道保持方法依赖高精度模型且计算负荷大的局限性，提出一种基于深度确定性策略梯度（DDPG）算法的车道保持辅助策略，融合了车辆动力学模型与深度强化学习（DRL）框架，并设计了一种考虑任务完成度和横向误差的奖励函数以优化模型训练收敛速度和跟踪精度，从而在不同弯道场景下实现更为精准和高效的车道保持性能。仿真验证结果表明，相较于传统的模型预测控制（MPC）方法，所提出方法的横向误差绝对值均值较MPC降低了42%~72%。

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