基于深度强化学习的智能感知行人与自主车辆安全防御

P. Vijayakumar, L. Deborah, S. Rajkumar
{"title":"基于深度强化学习的智能感知行人与自主车辆安全防御","authors":"P. Vijayakumar, L. Deborah, S. Rajkumar","doi":"10.4018/ijssci.291712","DOIUrl":null,"url":null,"abstract":"The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed obstructions at their respective locations with their relative distance, speed, and direction; such sensitive information forwards to the cloud server to predict the vehicle-hit, traffic congestion and road damages. Learn the behaviour of the state to produce an appropriate reward as the recommendation to avoid tragedy. Deep Reinforcement Learning and Q-network predict the complexity and uncertainty of the environment to generate optimal reward to states. Consequently, it activates automatic emergency braking and safe parking assistance to the vehicles. In addition, the proposed work provides safer transport for pedestrians and independent vehicles. Compared to the newer methods, the proposed system experimental results achieved 92.15% higher prediction rate accuracy. Finally, the proposed system saves many humans, animal lives from the vehicle hit, suggests drivers for rerouting to avoid unpredictable traffic, saves fuel consumption, and avoids carbon emission.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception\",\"authors\":\"P. Vijayakumar, L. Deborah, S. Rajkumar\",\"doi\":\"10.4018/ijssci.291712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed obstructions at their respective locations with their relative distance, speed, and direction; such sensitive information forwards to the cloud server to predict the vehicle-hit, traffic congestion and road damages. Learn the behaviour of the state to produce an appropriate reward as the recommendation to avoid tragedy. Deep Reinforcement Learning and Q-network predict the complexity and uncertainty of the environment to generate optimal reward to states. Consequently, it activates automatic emergency braking and safe parking assistance to the vehicles. In addition, the proposed work provides safer transport for pedestrians and independent vehicles. Compared to the newer methods, the proposed system experimental results achieved 92.15% higher prediction rate accuracy. Finally, the proposed system saves many humans, animal lives from the vehicle hit, suggests drivers for rerouting to avoid unpredictable traffic, saves fuel consumption, and avoids carbon emission.\",\"PeriodicalId\":432255,\"journal\":{\"name\":\"Int. J. Softw. Sci. Comput. Intell.\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Softw. Sci. Comput. Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijssci.291712\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Softw. Sci. Comput. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijssci.291712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

光探测和测距(LiDAR)传感器利用其相对距离、速度和方向跟踪每个感测障碍物的相应位置;这些敏感信息被转发到云服务器,用于预测车辆碰撞、交通拥堵和道路损坏情况。学习国家的行为,产生适当的奖励作为避免悲剧的建议。深度强化学习和q -网络预测环境的复杂性和不确定性,以产生对状态的最佳奖励。因此,它启动自动紧急制动和安全停车辅助车辆。此外,建议的工作为行人和独立车辆提供更安全的交通。实验结果表明,与较新的预测方法相比,该方法的预测准确率提高了92.15%。最后,该系统从车辆撞击中拯救了许多人类和动物的生命,建议司机改变路线以避免不可预测的交通,节省了燃料消耗,并避免了碳排放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep Reinforcement Learning-Based Pedestrian and Independent Vehicle Safety Fortification Using Intelligent Perception
The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed obstructions at their respective locations with their relative distance, speed, and direction; such sensitive information forwards to the cloud server to predict the vehicle-hit, traffic congestion and road damages. Learn the behaviour of the state to produce an appropriate reward as the recommendation to avoid tragedy. Deep Reinforcement Learning and Q-network predict the complexity and uncertainty of the environment to generate optimal reward to states. Consequently, it activates automatic emergency braking and safe parking assistance to the vehicles. In addition, the proposed work provides safer transport for pedestrians and independent vehicles. Compared to the newer methods, the proposed system experimental results achieved 92.15% higher prediction rate accuracy. Finally, the proposed system saves many humans, animal lives from the vehicle hit, suggests drivers for rerouting to avoid unpredictable traffic, saves fuel consumption, and avoids carbon emission.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams Artificial Intelligence Techniques to improve cognitive traits of Down Syndrome Individuals: An Analysis TA-WHI: Text Analysis of Web-Based Health Information Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach Model-Based Method for Optimisation of an Adaptive System
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1