Multi-Sensor Deep Integration for Next Generation Land Vehicle Positioning and Navigation
Multi-Sensor Deep Integration for Next Generation Land Vehicle Positioning and Navigation
批准号:
RGPIN-2015-06493
负责人:
Noureldin, Aboelmagd
金额:
$2.19万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2016
资助国家:
加拿大
项目状态:
已结题
起止时间:
2016-01-01 至 2017-12-31
中文摘要
目前的车载定位导航系统主要依靠全球定位系统(GPS)为驾驶员提供路径诱导信息。它们已经成为许多新车型不可或缺的一部分,它们的设计理念已经演变为包括功能,使它们不仅仅是一个车辆定位和地图显示系统。未来的车载POS/NAV技术将集成多个传感器和系统,以提高驾驶效率,增强驾驶员的体验和安全性。此外,智能交通系统(ITS)需要一个能够提供亚米级精度的POS/NAV系统,以使包括协作车辆在内的新兴ITS技术能够有效运行,以提高城市机动性和协作避免碰撞。
基于GPS的POS/NAV系统存在卫星信号阻塞、干扰和地面车辆在城市峡谷中经常遇到的多径等问题。与车辆运动传感器(速度计、加速计和陀螺仪)的集成可在短时间内增强性能。然而,上述应用所需的精度水平,即95%的时间精度低于米,尚未达到。陆上车辆现在配备了视觉摄像头、雷达和光检测和测距(LiDAR)设备,用于盲点显示和前向碰撞和偏离车道警告系统。这些技术的出现为进一步提高POS/NAV系统的精度提供了一个诱人的机会。
这项研究的最终目标是开发下一代集成的多传感器POS/NAV系统,能够为陆地车辆提供亚米级精度的无缝定位。该项目将设计一种新的POS/NAV范例,扩展当前GPS接收器技术的能力,以便在具有挑战性的城市峡谷中稳健地处理GPS卫星信号。它还将使GPS与单一平台上范围更广的传感器和互补特性的系统集成在一起。核心开发将基于一种可重新配置的GPS软件接收器,该接收器将在本研究中开发,以便能够访问GPS接收器内的低电平信号可观测物。
具有连续亚米精度的POS/NAV系统将在广泛的应用中产生重大影响。例如,汽车行业将受益于POS/NAV系统,该系统可以提高驾驶效率,增强驾驶员的体验和安全性。拟议的POS/NAV技术应为不同的智能交通系统应用和服务保持可靠和稳健的性能。它将使车辆司机、智能交通系统和道路网络基础设施之间能够有效地互动。在国防和矿业中广泛使用的无人驾驶车辆也将受益于拟议的技术。
英文摘要
Present land vehicle positioning and navigation (POS/NAV) systems provide drivers with route guidance information relying mostly on the Global Positioning System (GPS). They have become indispensable in many of the new automobile models, and their design concepts have evolved to include functionalities that make them more than just a vehicle location and map displaying system. Future land vehicle POS/NAV technology will integrate number of sensors and systems to increase the driving efficiency and enhance both driver’s experience and safety. In addition, intelligent transportation systems (ITS) require a POS/NAV system that can provide sub-meter accuracy to enable efficient operation of newly emerging ITS technologies including cooperative vehicles for improved urban mobility and for cooperative collision avoidance.
GPS – based POS/NAV systems suffer from satellite signal blockage, interference and multipath usually experienced by land vehicles in urban canyons. Integration with vehicle motion sensors (speedometers, accelerometers and gyroscopes) enhances the performance for short period of time. However, the required level of accuracy for the above applications, which is sub-meter accuracy 95% of the time, has not been yet achieved. Land vehicles are now equipped with vision cameras, Radar and light detection and ranging (LiDAR) devices utilized for blind spot display and forward collision and lane departure warning systems. The availability of these technologies provides an attractive opportunity to further increase the POS/NAV system accuracy.
The ultimate goal of this research is the development of next generation integrated multi-sensor POS/NAV system capable of offering seamless positioning at sub-meter level of accuracy for land vehicles. This project will device a new POS/NAV paradigm that expands the capabilities of current GPS receiver technology for robust processing of GPS satellite signals in challenging urban canyons. It will also enable the integration of GPS with wider range of sensors and systems of complimentary characteristics on a single platform. The core development will be based on a reconfigurable GPS software receiver that will be developed in this research to enable access to the low-level signal observables inside the GPS receiver.
A POS/NAV system with continuous sub-meter accuracy shall have a significant impact in wide range of applications. For instance, car industry will benefit from a POS/NAV system that increases driving efficiency and enhances driver’s experience and safety. The proposed POS/NAV technology shall maintain reliable and robust performance for different ITS applications and services. It will enable efficient interaction between the vehicle drivers, the ITS and the road network infrastructure. Unmanned vehicles widely used in national defence and mining industry will also benefit from the proposed technology.
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