Towards More Autonomy for Unmanned Vehicles: Situational Awareness and Decision Making under Uncertainty

实现无人驾驶车辆的更多自主性：不确定性下的态势感知和决策

• 批准号: EP/J011525/1
• 负责人: Wen-Hua Chen
• 金额: $ 128.21万
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ011525%2F1
• 关键词: Towards; More; Autonomy; Unmanned; Vehicles

It is anticipated that unmanned vehicles will be widely used within military and civilian operations and have a profound influence in our daily life in near future. Before fully realising the potential that unmanned vehicles bring, it is reasonably expected that to make unmanned vehicles accepted by users, the public and regulatory authorities, they shall achieve a similar level of safety as human operated systems. Among many others, a fundamental requirement for an unmanned vehicle is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Therefore, situational awareness and decision making are two of the most important enabling technologies for safe operation of unmanned vehicles. To a large extent, they determine the level of autonomy and intelligence of an unmanned vehicle. Compared with a human driver or pilot residing in the vehicle, a major safety concern is the inevitable reduction in situational awareness of the unmanned vehicle operator remotely located in a control station. Unmanned vehicles operate in a dynamic, unpredictable environment with incomplete (or inaccurate) sensory information, which creates many challenges in situational awareness and decision making. Probabilistic and bounded approaches are widely used to represent uncertainty with a known distribution or with a known upper and lower bounds respectively. Situational awareness includes the perception of the objects in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future. For example, in projection of the near status of moving objects of interest, any initial uncertainty associated with perception and comprehension will expand exponentially with the increase of the projection time span. However, it is possible to significantly reduce the uncertainty by utilising the information in the world model such as the operation environment, the Rules of the Road (or of the Air) and the properties of an identified object. For probabilistic uncertainty, this makes the Gaussian distribution assumption invalid, which is fundamental for most of the current statistical approaches such as Kalman filtering. Under the Gaussian distribution assumption, the estimated state about a moving object can be presented by its mean with a variance, and a symmetric uncertain region can be defined with the mean located at the centre (under a specified confidence level such as 99%). The introduction of knowledge (e.g. constraints due to the roadway layout) makes this not true anymore. To address the challenge of non-Gaussian distributions imposed by making use of information from the world model, a rigorous Bayesian learning framework will be developed for pooling all the knowledge from the world model and measurement data to provide a better estimate of the environment, and to propagate the uncertain regions with projection time. Reachability analysis will be developed for bounded uncertainty for worst case analysis, where the uncertainty will be reduced using constraints from the world model. Hazard analysis will be carried out to identify any potential risk. The key idea is to take a proactive approach to prevent any emergent situation through improving situational awareness reasoning and decision making. The estimates and associated uncertain region provided by the situational awareness will be fed to novel decision making and planning tools. The research activities will be strongly supported and verified by experimental tests on small scale ground and aerial vehicles. This project aims to significantly improve the level of safety of unmanned vehicle operation and to bridge the gap between the development and deployment of unmanned vehicles in real world applications, which is a strategically important area for new business growth.

可以预见，在不久的将来，无人驾驶车辆将广泛应用于军事和民用领域，并对我们的日常生活产生深远的影响。在充分认识到无人驾驶汽车所带来的潜力之前，有理由期望，要使无人驾驶汽车被用户、公众和监管机构所接受，它们应达到与人类操作系统相似的安全水平。其中，无人驾驶飞行器的一个基本要求是能够以安全、及时和适当的方式应对内部和外部变化。因此，态势感知和决策是无人机安全运行的两个最重要的使能技术。在很大程度上，它们决定了无人驾驶车辆的自主性和智能水平。与居住在车辆中的人类驾驶员或飞行员相比，主要的安全问题是远程位于控制站中的无人驾驶车辆操作员的态势感知不可避免地降低。无人驾驶车辆在动态、不可预测的环境中运行，具有不完整（或不准确）的传感信息，这给态势感知和决策带来了许多挑战。概率方法和有界方法被广泛用于表示具有已知分布或分别具有已知上界和下界的不确定性。情境意识包括在一定的时间和空间范围内感知环境中的物体，理解它们的意义并预测它们在不久的将来的状态。例如，在感兴趣的移动对象的附近状态的投影中，与感知和理解相关联的任何初始不确定性将随着投影时间跨度的增加而指数地扩展。然而，通过利用世界模型中的信息，例如操作环境，道路规则（或空中规则）和识别对象的属性，可以显著降低不确定性。对于概率不确定性，这使得高斯分布假设无效，这是当前大多数统计方法（如卡尔曼滤波）的基础。在高斯分布假设下，运动目标的估计状态可以由其均值和方差表示，并且可以定义一个对称的不确定区域，其中均值位于中心（在指定的置信水平下，例如99%）。知识的引入（例如，由于道路布局的约束）使这不再正确。为了解决利用世界模型的信息所带来的非高斯分布的挑战，将开发一个严格的贝叶斯学习框架，用于汇集世界模型和测量数据的所有知识，以提供对环境的更好估计，并随着投影时间传播不确定区域。可达性分析将针对最坏情况分析的有界不确定性进行开发，其中将使用世界模型的约束来降低不确定性。将进行危害分析以识别任何潜在风险。其核心思想是采取积极主动的方法，通过提高态势感知、推理和决策来预防任何紧急情况。由态势感知提供的估计和相关的不确定区域将被馈送到新的决策和规划工具。研究活动将得到小规模地面和空中飞行器实验测试的有力支持和验证。该项目旨在显著提高无人驾驶汽车运营的安全水平，并弥合无人驾驶汽车在真实的世界应用中的开发和部署之间的差距，这是新业务增长的重要战略领域。

项目成果

Unmanned ground operations using semantic image segmentation through a Bayesian network

• DOI: 10.1109/icuas.2016.7502572
• 发表时间: 2016-07
• 期刊: 2016 International Conference on Unmanned Aircraft Systems (ICUAS)
• 作者: M. Coombes;William Eaton;Wen‐Hua Chen

Colour based semantic image segmentation and classification for unmanned ground operations

用于无人地面操作的基于颜色的语义图像分割和分类

• DOI: 10.1109/icuas.2016.7502570
• 发表时间: 2016
• 作者: Coombes M

Reachability analysis of landing sites for forced landing of a UAS in wind using trochoidal turn paths

使用余摆线转弯路径对无人机在风中迫降着陆点的可达性分析

• DOI: 10.1109/icuas.2015.7152276
• 发表时间: 2015
• 作者: Coombes M

New Multiple-Target Tracking Strategy Using Domain Knowledge and Optimization

• DOI: 10.1109/tsmc.2016.2615188
• 发表时间: 2017-04-01
• 期刊: IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
• 影响因子: 8.7
• 作者: Ding, Runxiao;Yu, Miao;Chen, Wen-Hua
• 通讯作者: Chen, Wen-Hua

Data-driven situation awareness algorithm for vehicle lane change

数据驱动的车道变换态势感知算法

• DOI: 10.1109/itsc.2016.7795677
• 发表时间: 2016
• 作者: Dewei Yi