Tyre-Road Friction Coefficient Estimation using Maximum Entropy

使用最大熵估计轮胎-路面摩擦系数

• 批准号: EP/V010778/1
• 负责人: William Midgley
• 金额: $ 2.39万
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV010778%2F1
• 关键词: Tyre; Road; Friction; Coefficient; Estimation

Current trends toward automation include the automation of heavy goods vehicles. Heavy goods vehicles have long stopping distances which depend on the state of the road ahead of them. Computer vision can be used to determine the properties of the road ahead of the vehicle, allowing the possibility of determining where the vehicle should go in an emergency stopping manoeuvre. However, the estimate provided by computer vision of the properties of the road is an estimate, meaning that it has some uncertainty associated with it. Without taking this uncertainty into account, the actual stopping distance of the vehicle might be much longer than anticipated. The internal architecture for making vehicle motion planning decisions, integrating estimates of friction properties from the computer vision system, has not been clearly defined. In addition, a method is required for defining and understanding the uncertainty of the friction property estimates from the computer vision system and building this into the stopping distance estimation algorithm. This overseas travel grant seeks to fund a visit to Chalmers University of Technology in Gothenburg, Sweden. The grant will enable close collaboration between Dr Midgley and the vehicle dynamics group at Chalmers. It will also allow technical discussions with Volvo Trucks, who have an interest in the area of heavy vehicle automation. Dr Midgley will be able to work intensively on the architecture and distance estimation problems, while communicating freely with researchers from Chalmers and Volvo Trucks, and build links for future collaborative research projects.

目前自动化的趋势包括重型货车的自动化。重型货车的停车距离较长，视乎前面道路的情况而定。计算机视觉可用于确定车辆前方道路的特性，从而有可能确定车辆在紧急停车操作中应该去哪里。然而，由计算机视觉提供的道路属性的估计是一个估计，这意味着它具有一些与之相关的不确定性。如果不考虑这种不确定性，车辆的实际停车距离可能比预期的要长得多。用于做出车辆运动规划决策的内部架构，整合来自计算机视觉系统的摩擦特性估计，尚未明确定义。此外，需要一种方法来定义和理解来自计算机视觉系统的摩擦特性估计的不确定性，并将其构建到停止距离估计算法中。这笔海外旅费赠款旨在资助访问瑞典哥德堡的查尔默斯科技大学。这笔赠款将使米奇利博士和查尔默斯的车辆动力学小组密切合作。它还将允许与对重型车辆自动化领域感兴趣的沃尔沃卡车进行技术讨论。Midgley博士将能够深入研究建筑和距离估计问题，同时与来自查尔默斯和沃尔沃卡车的研究人员自由交流，并为未来的合作研究项目建立联系。

项目成果

Model-free Road Friction Estimation using Machine Learning

使用机器学习进行无模型道路摩擦力估计

• DOI: 10.1109/icm54990.2023.10102087
• 发表时间: 2023
• 作者: Midgley W