An Improved Deep Reinforcement Learning Algorithm for Path Planning in Unmanned Driving

强化学习 计算机科学 运动规划 人工智能 路径(计算) 机器学习 汽车工业 过度拟合 算法 人工神经网络 机器人 工程类 程序设计语言 航空航天工程
作者
Kai Yang,Li Liu
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:12: 67935-67944
标识
DOI:10.1109/access.2024.3400159
摘要

In the domain of intelligent transportation systems, the advent of autonomous driving technology represents a critical milestone, profoundly shaping the automotive industry's evolutionary path. This technology's core, particularly the algorithms facilitating driverless path planning, has attracted significant scholarly interest. This paper presents an advanced Deep Reinforcement Learning algorithm for Path Planning (DRL-PP), designed to rectify the shortcomings inherent in existing path planning techniques. Considering the complex nature of the environment, the DRL-PP algorithm is meticulously crafted to ascertain optimal actions, thereby effectively reducing the propensity for overfitting. The algorithm harnesses the capabilities of deep reinforcement learning, utilizing neural networks to identify the most advantageous action corresponding to a specific state. It then constructs an optimal action sequence, extending from the vehicle's initial position to its designated target. Additionally, the algorithm enhances the reward function by incorporating data pertinent to the objective. This refinement enables the nuanced differentiation of action values based on dynamically adjusted reward metrics, thereby augmenting the efficiency of the action selection process and yielding improved results in path planning. Empirical results validate the algorithm's proficiency in stabilizing the reward metric while minimizing exploratory steps, consistently surpassing comparative models in path-finding effectiveness.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
Iolite完成签到,获得积分10
2秒前
小马甲应助Shuaibin_Pei采纳,获得10
2秒前
量子星尘发布了新的文献求助10
2秒前
hbhbj完成签到,获得积分10
3秒前
raolixiang完成签到,获得积分10
3秒前
5秒前
打工人不酷完成签到 ,获得积分10
6秒前
7秒前
9秒前
背后丹妗发布了新的文献求助10
9秒前
10秒前
10秒前
小凯同学完成签到 ,获得积分10
10秒前
hanleiharry1发布了新的文献求助10
12秒前
12秒前
12秒前
善良冷松发布了新的文献求助10
12秒前
14秒前
在水一方应助一定行采纳,获得10
15秒前
15秒前
15秒前
NexusExplorer应助快乐一江采纳,获得10
16秒前
16秒前
科研通AI5应助Lcccccc采纳,获得10
16秒前
在水一方应助杰2580采纳,获得10
19秒前
幸福大白发布了新的文献求助30
19秒前
Jasmine发布了新的文献求助10
19秒前
20秒前
善良冷松完成签到,获得积分10
20秒前
20秒前
善学以致用应助fengliurencai采纳,获得10
21秒前
个别完成签到,获得积分10
22秒前
22秒前
22秒前
23秒前
sihanzhiyu完成签到,获得积分20
24秒前
24秒前
wdy111应助ASZXDW采纳,获得20
26秒前
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989390
求助须知:如何正确求助?哪些是违规求助? 3531487
关于积分的说明 11254109
捐赠科研通 3270153
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809174