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

在过去十年中，由于汽车制造商（如福特、奥迪和梅赛德斯）和科技公司（如b谷歌和Uber）的重大发展和进步，自动驾驶汽车（SVD）在军事和民用应用方面都受到了广泛关注。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环境（例如激光雷达、摄像头、雷达、全球导航卫星系统（GNSS）、惯性传感器和里程表），复杂的算法来整理和解释获取的多传感器数据，以及强大的处理器来执行实施算法并实时规划安全的前进路径。***除了成本、信任、可靠性、安全性和道德问题外，SDV的广泛采用还存在重大的技术障碍。例如，这些车辆所采用的定位技术不够精确，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高度详细的周围环境地图。因此，开发一种能够提供高精度定位信息、高分辨率周围环境映射和可靠路径规划的多传感器系统校准、数据融合和异构数据处理工作流程仍然缺乏。换句话说，多传感器系统板的校准不佳，涉及的GNSS、惯性和视觉传感器的集成和数据融合，在没有GNSS信息的情况下对车辆进行精确定位，高效处理大量多传感器数据以详细绘制周围环境，智能信息提取用于静态/动态障碍物检测，汽车行业和科技公司在追求自己的自动驾驶汽车目标时，还没有完全理解和解决实时/近实时决策和路径规划问题。为了克服这些挑战，使自动驾驶汽车能够安全上路，本提案旨在开发一个综合框架，用于多传感器系统校准、精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映射、决策和路径规划，同时满足自动驾驶汽车的需求。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，并促进其在非受控环境中的广泛采用。

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.