Multi-sensor data fusion for autonomous vehicle situational awareness

自动驾驶汽车（AVs）有望对长期存在的运力、拥堵、安全、环境影响和效率等交通问题产生变革性影响。英国互联场所弹射器公司估计，到2025年，全球基于互联自动驾驶汽车（cav）的智能移动市场将达到9000亿英镑。自动驾驶汽车成功运行的关键是态势感知（SA）能力。由于缺乏用于SA的单一技术，必须考虑使用互补的多种传感器，包括用于(1)PNT的传感器，例如全球导航卫星系统（GNSS）、航位推断传感器（例如惯性测量单元、里程表、磁力计）和机会信号；(2)远程和环境传感器，如摄像头、激光雷达、雷达和超声波传感器；(3)空间数据库；(4)整理和解释多传感器数据的算法；(5)执行算法并实时规划安全前进路径的强大处理器。除了成本、安全和道德之外，还有重大的技术挑战。例如，目前的自动驾驶系统（SA）价格昂贵，无法满足支持关键任务自动驾驶车辆运行所需的PNT精度、可靠性、连续性和服务可用性；(2)无法提供所需的近实时高分辨率地图；(3)依赖于天气相关传感器，如LiDAR，以及用于障碍物检测、环境传感和导航的低分辨率路线图。(4)两组传感器（PNT和遥感）不能相互作用以提供所需的导航性能。迄今为止，测试和随后的成功主要是在良好的天气条件下进行的，并使用了目前尚未广泛获得的具有所需分辨率的路线图。因此，本博士研究的目标是开发高性能的SA模型和算法(a)更好地解释天气效应的影响，并且是独立的，能够提供PNT精度，完整性，连续性和可用性的水平，(b)低成本，(c)融合所有可用的传感器数据来实现完整的SA。将开发用于表征物理复杂城市环境中各种传感器性能的新方法（即热图），并将其与多目标优化算法一起使用，以制定和测试SA的最佳传感器融合系统架构。EPSRC研究领域：传感器和仪器仪表，基础设施和城市系统

Autonomous Vehicles (AVs) are expected to deliver a transformative impact on the long standing transport problems of capacity, congestion, safety, environmental impact and efficiency. The UK Connected Places Catapult estimates the global Connected Autonomous Vehicles (CAVs) based smart mobility market at £900 billion by 2025. Critical to successful operation of AVs is the capability for Situational Awareness (SA). Because of a lack of a single technology for SA, consideration must be given to the use of complementary multiple sensors including those for (1) PNT such as Global Navigation Satellite Systems (GNSS), Dead Reckoning sensors (e.g. Inertial Measurement Units, odometers, magnetometers) and signals of opportunity; (2) remote and environmental sensors such as cameras, LiDAR, RADAR and ultrasonic sensors, (3) Spatial databases, (4) algorithms to collate and interpret the multi-sensor data, and (5) powerful processors to execute the algorithms and plan a safe path forward in real-time. In addition to cost, security, and ethics, there are significant technical challenges. For example, the current SA systems are (1) expensive and do not the meet the required PNT accuracy, trustworthiness, continuity and service availability to support mission critical autonomous vehicle operations, (2) cannot provide the required high resolution maps in near real-time, (3) rely on weather-dependent sensors, such as LiDAR, and low resolution road maps for obstacle detection, environment sensing and navigation, and (4) the two groups of sensors (PNT and remote sensing) do not interact to provide the required navigation performance. To date, tests and ensuing successes have largely been demonstrated in good weather and using road maps with the required level of resolution, not currently widely available. Therefore, the objectives of this PhD research are to develop high performance SA models and algorithms that (a) better account for the effects of the weather effects and are self-contained and capable of delivering the levels of PNT accuracy, integrity, continuity and availability, (b) low cost, and (c) fuse all available sensor data to realise complete SA. Novel methods for characterising the performance of various sensors in physically complex city environments (i.e. heat mapping) will be developed and used together multi-objective optimisation algorithms to formulate and test the best sensor fusion system architecture for SA. EPSRC Research Areas: Sensors and instrumentation, Infrastructure and urban systems