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

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:
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
项目状态:
已结题
起止时间:
2011 至 --

项目摘要

项目成果

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中文摘要
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英文摘要
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.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
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]
通讯作者: 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
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
7
    COG-MHEAR: Towards cognitively-inspired 5G-IoT enabled, multi-modal Hearing Aids
    • 批准号:
      EP/T021063/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $415.26万
    • 财政年份:
      2021
    • 负责人:
      Amir Hussain
    • 依托单位:
    Towards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devices (AV-COGHEAR)
    • 批准号:
      EP/M026981/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $53.29万
    • 财政年份:
      2015
    • 负责人:
      Amir Hussain
    • 依托单位:
    Industrial CASE Account - Stirling 2009
    • 批准号:
      EP/H501584/1
    • 项目类别:
      Training Grant
    • 资助金额:
      $16.64万
    • 财政年份:
      2009
    • 负责人:
      Amir Hussain
    • 依托单位:
    Industrial CASE Account - Stirling 2008
    • 批准号:
      EP/G501750/1
    • 项目类别:
      Training Grant
    • 资助金额:
      $8.13万
    • 财政年份:
      2009
    • 负责人:
      Amir Hussain
    • 依托单位:
    国内基金
    海外基金
    Neural Process模型的多样化高保真技术研究
    磁转动超新星爆发中weak r-process的关键核反应
    多臂Bandit process中的Bayes非参数方法
    • 批准号:
      71771089
    • 项目类别:
      面上项目
    • 资助金额:
      48.0万元
    • 批准年份:
      2017
    • 负责人:
      吴贤毅
    • 依托单位: