Dual Process Control Models in the Brain and Machines with Application to Autonomous Vehicle Control

大脑和机器中的双过程控制模型在自主车辆控制中的应用

• 批准号: EP/I009310/1
• 负责人: Amir Hussain
• 金额: $ 44.93万
• 依托单位: University of Stirling
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI009310%2F1
• 关键词: Dual; Process; Control; Models; Brain

The field of autonomous vehicle control (AVC) is a rapidly growing one which promises improved performance, fuel economy, emission levels, comfort and safety.Application of conventional control methods can generate adequate results under restricted circumstances,but have high design and computational costs and are fragile under real environmental changes (winds, proximity of other vehicles etc).There is therefore a pressing need for alternative approaches to AVC.One particularly promising alternative is to break the task into a set of sub-tasks,each valid over a restricted range of conditions, and to switch between them when required.Dr Hussain's group in Stirling has been developing a novel framework for such'modular learning controllers'over the last few years.The problem of selecting from amongst a set of actions or behaviours is also a central problem for animals.There is growing evidence that a set of central brain nuclei -the basal ganglia- are used by all vertebrates to help solve this problem.Research in Prof Gurney's lab has,over the last decade,been developing computational models of how the basal ganglia support behavioural selection.Thus,we believe that the basal ganglia act as a central 'selector' or 'switch' in all vertebrate brains,in that they examine requests for behaviour and allow the most urgent or salient requests to be selected for behavioural expression Given the similarity between the two problems' domains of AVC and action selection in animals, this project aims to leverage new results from psychology and neurobiology (discovered in Prof Gurney's lab) and apply them to the AVC controllers under development in Dr Hussain's group.One aspect of action selection which appears particularly promising in this respect has to do with there being two general modes of behavioural selection.To see this,consider the following scenarios.First,imagine making tea soon after getting out of bed in the morning in your own kitchen.You probably know exactly what to do without having to consciously be aware of it--the location of the tea,milk,sugar,kettle and water-tap are all well learned, as is the motor actions required to interact with the objects in these locations.Introspection after the event leads us to use terms such as;`I did it in my sleep' or `I was on auto-pilot'.Now consider doing the same task if you are staying at a friend's house for the first time.A completely different strategy appears to be used.Thus,we have to be alert, explore, and use high level cognitive knowledge that we hope generalises well (for example,we hope the tea will be in a cupboard near the sink, not in the living room)These two modes of control are well recognised in the psychological literature as automatic and controlled or executive processing respectively.There is also growing neurobiological evidence for the existence of different control regimes, supported by different brain systems.In addition, the new AVC systems developed at Stirling have two major components:a high level 'supervisory' controller and a set of basic (but adaptable) controllers that direct the actual vehicle behaviour.We believe the similarities with the biological notions of executive and automatic control are highly indicative of a mutually fruitful interaction between neuroscientific and control theoretic domains in this regard.Thus, while our general aim is to exploit a range of similarities between systems in control engineering and the animal brain, we will focus specifically on the concepts of automatised and controlled (or executive) processing and how they might map onto modular AVC solutions of the kind described above.The outcome should be a new generation real-time AVC controller, more directly inspired by the biological ideas. We will work with our industrial partners (Industrial Systems Control and SciSys) to evaluate the benefits of these novel controllers within the context of regular road driving and planetary rover vehicles.

自动车辆控制(AVC)是一个快速发展的领域，它承诺改善性能、燃油经济性、排放水平、舒适性和安全性。传统控制方法的应用在有限的条件下可以产生足够的结果，但具有较高的设计和计算成本，并且在真实环境变化(风、其他车辆的接近等)下很脆弱。因此，迫切需要替代的方法来实现自动车辆控制。一个特别有希望的替代方案是将任务分解为一组子任务，每个子任务在有限的条件范围内有效，在过去的几年里，侯赛因博士在斯特林的研究小组一直在开发一种新的框架，用于这种“模块化学习控制器”。从一组动作或行为中进行选择的问题也是动物的一个中心问题。越来越多的证据表明，所有脊椎动物都使用一组中央大脑核团--基底节--来帮助解决这一问题。在过去的十年里，格尼教授实验室的研究一直在开发计算模型，说明基底节是如何支持行为选择的。因此，我们认为，基底节在所有脊椎动物的大脑中扮演着中央‘选择器’或‘开关’的角色。考虑到两个问题的AVC领域和动物的动作选择之间的相似性，该项目旨在利用心理学和神经生物学的新结果(在Gurney教授的实验室中发现)，并将它们应用于Hussain博士团队正在开发的AVC控制器。动作选择的一个方面在这方面似乎特别有希望。为了理解这一点，请考虑以下场景。想象一下，早上起床后不久，在你自己的厨房里泡茶。你可能不需要有意识地知道该做什么--茶、牛奶、糖、水壶和水龙头的位置都很好地掌握了，以及与这些位置上的物体互动所需的运动动作。事后的反思让我们使用了这样的术语：“我是在睡梦中做的”或“我是在自动驾驶状态下做的”。如果你是第一次住在朋友家，现在考虑做同样的任务。这似乎是一种完全不同的策略。因此，我们必须保持警惕，探索并使用我们希望能很好地概括的高级认知知识(例如，我们希望茶会放在水槽附近的橱柜里，而不是在客厅里)。这两种控制模式在心理学文献中分别被认为是自动的、受控的或执行过程。还有越来越多的神经生物学证据表明存在不同的控制机制。由不同的大脑系统支持。此外，在斯特林开发的新AVC系统有两个主要组件：一个高级的‘监控’控制器和一组指导实际车辆行为的基本(但可适应的)控制器。我们相信，与执行和自动控制的生物学概念的相似性高度表明在这方面神经科学和控制理论领域之间的相互富有成效的互动。因此，尽管我们的总体目标是利用控制工程系统和动物大脑之间的一系列相似之处，我们将特别关注自动化和受控(或执行)处理的概念，以及它们如何映射到上述类型的模块化AVC解决方案。结果应该是新一代实时AVC控制器，更直接地受到生物学思想的启发。我们将与我们的工业合作伙伴(工业系统控制和Science Sys)合作，在常规道路驾驶和行星漫游车的背景下评估这些新型控制器的好处。

项目成果

Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images

• DOI: 10.1007/s12559-016-9405-9
• 发表时间: 2016-04
• 期刊: Cognitive Computation
• 影响因子: 5.4
• 作者: F. Gao;Fei Ma;Yaotian Zhang;J. Wang;Jinping Sun;Erfu Yang;A. Hussain

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach

合成孔径雷达图像中基于视觉注意模型的车辆目标检测：一种新方法

• DOI: 10.1007/s12559-014-9312-x
• 发表时间: 2014-12
• 期刊: Cognitive Computation
• 影响因子: 5.4
• 作者: Wang, Jun;Sun, Jinping;Yang, Erfu;Hussain, Amir
• 通讯作者: Hussain, Amir

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

基于改进深度卷积神经网络的SAR图像目标识别新算法

• DOI: 10.1007/s12559-018-9563-z
• 发表时间: 2019-12-01
• 期刊: COGNITIVE COMPUTATION
• 影响因子: 5.4
• 作者: Gao, Fei;Huang, Teng;Yang, Erfu
• 通讯作者: Yang, Erfu

Nutritional Evaluation of an EPA-DHA Oil from Transgenic Camelina sativa in Feeds for Post-Smolt Atlantic Salmon (Salmo salar L.).

• DOI: 10.1371/journal.pone.0159934
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Betancor MB;Sprague M;Sayanova O;Usher S;Metochis C;Campbell PJ;Napier JA;Tocher DR
• 通讯作者: Tocher DR

A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

一种基于增量半监督支持向量机的新型分类算法

• DOI: 10.1371/journal.pone.0135709
• 发表时间: 2015
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Gao F;Mei J;Sun J;Wang J;Yang E;Hussain A
• 通讯作者: Hussain A