Multi-Sensor Precise Positioning for Autonomous and Connected Vehicles

自动驾驶和联网车辆的多传感器精确定位

• 批准号: RGPIN-2020-03900
• 负责人: Noureldin, Aboelmagd
• 金额: $ 2.84万
• 依托单位: Royal Military College of Canada
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752332
• 关键词: Multi; Sensor; Precise; Positioning; Autonomous

Autonomous and connected vehicles (AV/CVs) promise to improve the safety and efficiency of our transportation system and bring new economic opportunities. They have the potential to reduce collisions, traffic congestion and emissions, and to improve mobility for a growing population. The increasing demand for AV/CVs is shaping the future of the automotive industry by connecting vehicles to one another and the surrounding infrastructure, transforming the in-vehicle experience and paving the way to autonomous driving. AV technology requires vehicle onboard intelligence relying on a suite of sensors and systems involving global navigation satellite systems (GNSS) including GPS, vehicle motion sensors (gyroscopes, accelerometers, and speedometers), cameras, light detection and ranging (LiDAR) and radar. On the other hand, CV technology involves communication between vehicles to share a common perception and awareness of other traffic participants. A key AV/CV system requirement is to have a highly reliable positioning to sub-meter lane-level accuracy. The availability of the above-mentioned sensors and systems in future AV/CVs provides an attractive opportunity to further increase the accuracy and reliability of the positioning system. The proposed research program aims to provide continuous, reliable and ultra-precise multi-sensor positioning for the safe operation of AV/CVs. The proposed research will enhance the positioning of AV/CVs on multiple fronts including (a) accurate positioning reaching decimeter levels of accuracy for lane-level navigation with high integrity; (b) advanced multi-sensor fusion for positioning anywhere for autonomy that adapts to changes in the environment; (c) machine intelligence algorithms to select the set of sensors and systems to be integrated to provide the most reliable and accurate positioning information in all operational environments; and (d) high data rate positioning information for fast response to meet the demand for safety-critical autonomous driving. This research program will enable the deployment of novel AV/CV positioning systems operating efficiently everywhere - even in degraded and challenging environments such as areas of GNSS signal blockage, darkness, absence of visual features, smoke, snow, fog, and rain while providing decimeter-level accuracy. From an autonomy perspective, this work is expected to have a significant impact on the acceptance and trust of targeted AV/CVs since it will increase reliability, decrease the failure rate expected in such degraded environments, and enhance safety and reliability. This research will also contribute to training highly qualified personnel (HQP) with expertise in multi-sensor fusion, machine intelligence and precise positioning. There is a strong demand for HQP with knowledge in these areas and their future employment will accelerate the translation of next-generation multi-sensory positioning and autonomous technologies to the Canadian industry.

自动驾驶和互联车辆(AV/CV)承诺提高我们交通系统的安全性和效率，并带来新的经济机会。它们有可能减少碰撞、交通拥堵和排放，并为不断增长的人口改善机动性。对汽车/简历日益增长的需求正在塑造汽车行业的未来，将汽车彼此连接起来，并将周围的基础设施连接起来，改变车内体验，为自动驾驶铺平道路。无人机技术需要车载智能，依赖于一套涉及全球导航卫星系统(GNSS)的传感器和系统，包括GPS、车辆运动传感器(陀螺仪、加速计和速度计)、摄像头、光探测和测距(LiDAR)和雷达。另一方面，CV技术涉及车辆之间的通信，以共享其他交通参与者的共同感知和意识。AV/CV系统的一个关键要求是具有高度可靠的定位到亚米级车道级的精度。上述传感器和系统在未来的无人机/简历中的可用性为进一步提高定位系统的精度和可靠性提供了一个有吸引力的机会。拟议的研究计划旨在为AV/CV的安全运行提供连续、可靠和超精密的多传感器定位。拟议的研究将加强汽车/履带车辆在多个战线的定位，包括(A)达到分米级别的精确定位，以实现高度完整性的车道级导航；(B)先进的多传感器融合，用于在任何地方定位，以适应环境的变化；(C)机器智能算法，以选择要集成的传感器和系统集，以在所有操作环境中提供最可靠和准确的定位信息；以及(D)高数据率定位信息，用于快速响应，以满足对安全至关重要的自动驾驶的需求。这项研究计划将使新的AV/CV定位系统能够在任何地方高效运行--即使是在GNSS信号受阻、黑暗、缺乏视觉特征、烟雾、雪、雾和雨等环境恶化和具有挑战性的环境中，也能提供分米级的精度。从自主的角度来看，这项工作预计将对目标AV/CV的接受和信任产生重大影响，因为它将提高可靠性，降低在这种降级环境中预期的故障率，并增强安全性和可靠性。这项研究还将有助于培养具有多传感器融合、机器智能和精确定位专业知识的高素质人才(HQP)。拥有这些领域知识的HQP需求旺盛，他们未来的就业将加速下一代多感官定位和自主技术向加拿大行业的转化。