Multi-Sensors Data Fusion for Navigation of Self-Driving Vehicles

用于自动驾驶车辆导航的多传感器数据融合

• 批准号: RGPIN-2018-04310
• 负责人: ELSHEIMY, NASER
• 金额: $ 3.13万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712914
• 关键词: Multi; Sensors; Data; Fusion; Navigation

During the last decade, self-driving vehicles (SVD) have received comprehensive attention in both military and civilian applications due to the significant developments and progress demonstrated by automobile manufacturers (e.g., Ford, Audi, and Mercedes) and technology companies (e.g., Google and Uber). The successful operation of autonomous vehicles relies on a combination of multi-sensors to determine their precise location and sense the environment around (e.g. LiDAR, Cameras, RADAR, Global Navigation Satellite Systems (GNSS), inertial sensors, and odometers), sophisticated algorithms to collate and interpret the acquired multi-sensor data, and powerful processors to execute the implemented algorithms and plan a safe path forward in real time. In addition to cost, trust, reliability, security, and ethical issues, there are significant technical hurdles on the path to widespread adoption of SDV. For example, the employed positioning technology in these vehicles is not accurate enough to be solely trusted and used in safety-critical situations. Moreover, existing digital mapping systems cannot provide highly-detailed maps of surrounding environments that support self-driving applications. Therefore, the development of a multi-sensor system calibration, data fusion, and heterogeneous data processing workflow which is capable of delivering high-accuracy positioning information, high-resolution mapping of the surrounding environment, and reliable path planning is still missing. In other words, the challenges introduced by having a poorly-calibrated multi-sensor system board, the integration and data fusion of the involved GNSS, inertial, and vision-based sensor, precise localization of the vehicle in the absence of GNSS information, efficient processing of large volume multi-sensor data for detailed mapping of the surrounding environment, intelligent information extraction for static/dynamic obstacle detection, and real-time/near real-time decision making and path planning have not been fully understood and addressed by the automotive industry and technology companies pursuing their own self-driving car ambitions. In order to overcome these challenges and prepare self-driving vehicles to hit the roads safely, this proposal aims at developing a comprehensive framework for multi-sensor system calibration, accurate multi-sensor data fusion, and efficient data processing, road/obstacle-related information extraction, highly-detailed mapping of surrounding environments, decision-making, and path planning while addressing the demands of autonomous self-driving vehicles. The proposed framework will provide significant economic, technological, and social benefits to Canadian government and automotive industry. Hence, it will increase the safety and reliability of SVD and promotes their wide adoption in uncontrolled environments.

在过去十年中，由于汽车制造商展示的重大发展和进步，自动驾驶车辆（SVD）在军事和民用应用中受到了广泛关注（例如，福特、奥迪和奔驰）和科技公司（例如，Google和Uber）。自动驾驶汽车的成功运行依赖于多传感器的组合，以确定其精确位置并感知周围的环境（例如LiDAR、照相机、RADAR、全球导航卫星系统（GNSS）、惯性传感器和里程计）、用于整理和解释所获取的多传感器数据的复杂算法，以及强大的处理器来执行所实现的算法并在真实的时间内规划安全的前进路径。 除了成本、信任、可靠性、安全性和道德问题之外，在广泛采用SDV的道路上还存在重大的技术障碍。例如，这些车辆中采用的定位技术不够准确，无法完全信任并用于安全关键情况。此外，现有的数字地图系统无法提供支持自动驾驶应用的周围环境的高度详细的地图。因此，开发的多传感器系统校准，数据融合，异构数据处理工作流程，这是能够提供高精度的定位信息，周围环境的高分辨率映射，和可靠的路径规划仍然缺失。换句话说，由于具有校准不良的多传感器系统板、所涉及的GNSS、惯性和基于视觉的传感器的集成和数据融合、在没有GNSS信息的情况下车辆的精确定位、用于周围环境的详细映射的大量多传感器数据的有效处理、用于静态/动态障碍物检测的智能信息提取、实时/近实时决策和路径规划尚未被汽车行业和追求自己的自动驾驶汽车雄心的技术公司完全理解和解决。 为了克服这些挑战并使自动驾驶车辆安全上路，该提案旨在开发一个全面的框架，用于多传感器系统校准，准确的多传感器数据融合，高效的数据处理，道路/障碍物相关信息提取，高度详细的周围环境地图，决策和路径规划，同时满足自动驾驶车辆的需求。拟议的框架将为加拿大政府和汽车工业提供重大的经济、技术和社会效益。因此，它将提高SVD的安全性和可靠性，并促进其在不受控制的环境中的广泛采用。