• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

利用道路信息和多头注意力模型预测自动驾驶车辆的驾驶舒适性。

Predicting driving comfort in autonomous vehicles using road information and multi-head attention models.

作者信息

Chen Zhengxian, Liu Yuqi, Ni Wenjie, Hai Han, Huang Chaosheng, Xu Boyang, Ling Zihan, Shen Yang, Yu Wenhao, Wang Huanan, Li Jun

机构信息

School of Vehicle and Mobility, Tsinghua University, 100084, Beijing, China.

Graduate School of Natural Sciences, Utrecht University, 3584CS, Utrecht, Netherlands.

出版信息

Nat Commun. 2025 Mar 19;16(1):2709. doi: 10.1038/s41467-025-57845-z.

DOI:10.1038/s41467-025-57845-z
PMID:40108170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11923197/
Abstract

Driving comfort is a crucial consideration in the automotive industry. In the realm of autonomous driving, comfort has always been a factor that requires continuous improvement. A common approach to improving driving comfort is through the optimization of local path planning. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios increase the possibility of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. This paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study established a road information-driving comfort dataset and devised prediction models using multi-head attention mechanism. The ensuing discussion elucidates the practical application of the model in path planning through examples and tests. Following the path optimized by the model, the vehicles exhibited a reduction in jerk. This research predicted driving comfort based on road information and integrated it with global path planning, which holds significant implications for autonomous driving navigation systems and provides a valuable reference for related research.

摘要

驾驶舒适性是汽车行业的一个关键考量因素。在自动驾驶领域，舒适性一直是需要不断改进的一个因素。提高驾驶舒适性的一种常见方法是通过优化局部路径规划。然而，必须认识到，包括交通流量和道路状况在内的宏观因素对舒适性有着重大影响。例如，复杂的交通场景增加了紧急制动的可能性，从而影响舒适性。因此，研究舒适性与全局路径规划之间的复杂相互作用变得至关重要。本文介绍了一种利用道路信息预测驾驶舒适性的方法和框架。该研究建立了一个道路信息-驾驶舒适性数据集，并使用多头注意力机制设计了预测模型。随后的讨论通过示例和测试阐明了该模型在路径规划中的实际应用。沿着该模型优化的路径行驶时，车辆的急动度有所降低。本研究基于道路信息预测驾驶舒适性，并将其与全局路径规划相结合，这对自动驾驶导航系统具有重要意义，并为相关研究提供了有价值的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/987a8d12ebfa/41467_2025_57845_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/9dbb6393c969/41467_2025_57845_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/9fbcd4a45264/41467_2025_57845_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/759cfb4c207e/41467_2025_57845_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/89593a6b68da/41467_2025_57845_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/7aee4d491fb6/41467_2025_57845_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/095015d29f4c/41467_2025_57845_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/09cf1f26e0e4/41467_2025_57845_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/987a8d12ebfa/41467_2025_57845_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/9dbb6393c969/41467_2025_57845_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/9fbcd4a45264/41467_2025_57845_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/759cfb4c207e/41467_2025_57845_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/89593a6b68da/41467_2025_57845_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/7aee4d491fb6/41467_2025_57845_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/095015d29f4c/41467_2025_57845_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/09cf1f26e0e4/41467_2025_57845_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96aa/11923197/987a8d12ebfa/41467_2025_57845_Fig8_HTML.jpg

相似文献

1
Predicting driving comfort in autonomous vehicles using road information and multi-head attention models.利用道路信息和多头注意力模型预测自动驾驶车辆的驾驶舒适性。
Nat Commun. 2025 Mar 19;16(1):2709. doi: 10.1038/s41467-025-57845-z.
2
Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots.基于注意力机制结合机器人多模态信息决策思想的自动驾驶车辆避障优化与路径规划研究
Front Neurorobot. 2023 Sep 22;17:1269447. doi: 10.3389/fnbot.2023.1269447. eCollection 2023.
3
Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.基于对弱势道路使用者碰撞伤害预测的自动驾驶车辆轨迹规划框架。
Accid Anal Prev. 2024 Aug;203:107610. doi: 10.1016/j.aap.2024.107610. Epub 2024 May 14.
4
A road surface reconstruction dataset for autonomous driving.面向自动驾驶的道路表面重建数据集。
Sci Data. 2024 May 6;11(1):459. doi: 10.1038/s41597-024-03261-9.
5
Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment.回旋线：动态城市环境中自动驾驶的集成分层框架。
Sensors (Basel). 2020 Sep 5;20(18):5053. doi: 10.3390/s20185053.
6
Real-time path planning for autonomous vehicle off-road driving.自动驾驶车辆越野行驶的实时路径规划。
PeerJ Comput Sci. 2024 Jul 24;10:e2209. doi: 10.7717/peerj-cs.2209. eCollection 2024.
7
Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data.基于自然驾驶研究数据中与安全相关的跟车事件，开发改进的自动预防制动系统。
Accid Anal Prev. 2022 Dec;178:106834. doi: 10.1016/j.aap.2022.106834. Epub 2022 Sep 21.
8
The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles.基于道路粗糙度和周围车辆乘客状态的制动压力和行驶方向确定系统（BDDS）。
Sensors (Basel). 2022 Jun 10;22(12):4414. doi: 10.3390/s22124414.
9
Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization.基于车道流注意力的长短期记忆网络与道路几何线性化的车辆轨迹预测
Sensors (Basel). 2021 Dec 6;21(23):8152. doi: 10.3390/s21238152.
10
TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.采用：面向互联和自动驾驶车辆的基于交通知识的导航系统。
Sensors (Basel). 2023 Jan 6;23(2):653. doi: 10.3390/s23020653.

引用本文的文献

1
Improving Intelligent Vehicle Control with a Prediction Model of Passenger Comfort Based on Postural Instability Parameters.基于姿势不稳定参数的乘客舒适度预测模型改善智能车辆控制
Sensors (Basel). 2025 Sep 5;25(17):5529. doi: 10.3390/s25175529.

本文引用的文献

1
Standards for passenger comfort in automated vehicles: Acceleration and jerk.自动驾驶车辆乘客舒适性标准：加速度和加加速度。
Appl Ergon. 2023 Jan;106:103881. doi: 10.1016/j.apergo.2022.103881. Epub 2022 Sep 2.
2
Path Planning for Autonomous Mobile Robots: A Review.自主移动机器人路径规划：综述。
Sensors (Basel). 2021 Nov 26;21(23):7898. doi: 10.3390/s21237898.
3
State-of-the-art in artificial neural network applications: A survey.人工神经网络应用的最新进展：一项综述。
Heliyon. 2018 Nov 23;4(11):e00938. doi: 10.1016/j.heliyon.2018.e00938. eCollection 2018 Nov.
4
Predicting passenger seat comfort and discomfort on the basis of human, context and seat characteristics: a literature review.基于人体、环境和座椅特性预测乘客座椅的舒适性与不舒适性：文献综述
Ergonomics. 2017 Jul;60(7):889-911. doi: 10.1080/00140139.2016.1233356. Epub 2016 Oct 15.
5
Spatial and temporal EEG dynamics of motion sickness.运动病的时空 EEG 动力学。
Neuroimage. 2010 Feb 1;49(3):2862-70. doi: 10.1016/j.neuroimage.2009.10.005. Epub 2009 Oct 13.