Machine Learning-based Multi-Sensor Positioning and Mapping for Autonomous Vehicles

自动驾驶汽车（AV）是汽车，公共汽车或卡车，可以在有限或没有人为干预的情况下行驶。它们结合了联合收割机传感器、感知系统和软件来控制、导航和安全驾驶车辆。自动驾驶汽车有望提高我们交通系统的效率，减少碰撞和交通拥堵，并提高我们不断增长的人口的流动性。随着世界继续朝着由自动驾驶汽车驱动的更高效的交通系统发展，对保证自动驾驶汽车高效和安全运行的新技术的需求日益增加。无人驾驶汽车的可靠性取决于自主性，包括关键的定位任务，这一任务应在所有环境中以高精度在任何地方准确提供。这项研究将能够为无人驾驶汽车开发和部署可靠的定位和测绘系统，其精度为分米级，具有高完整性。我们提出的解决方案将在所有操作条件下无缝地保持这种性能。与我们的行业合作伙伴Micro Engineering Tech一起，我们将为自动驾驶汽车的定位和映射引入新的范式。它将扩展现有技术的能力，并在单一平台上处理更广泛的互补传感器和系统。这项研究将彻底改变AV多传感器定位和映射。我们将开发独特的机器学习方法来管理和协调异构的AV传感器/系统，以实现高效和安全的自动驾驶。这项研究将通过提高安全性和可靠性以及降低降级环境中的故障率来显著影响对自动驾驶汽车的接受和信任。它还将有助于实现无处不在的定位技术，并提高加拿大在AV服务领域的地位。此外，高素质人才（HQP）的培训是这项研究计划的重要组成部分。一个由10名才华横溢的HQP组成的多元化小组将参与进来，为推进加拿大的AV技术做出贡献。

Autonomous vehicles (AVs) are cars, buses or trucks that can travel with limited or no human intervention. They combine sensors, perception systems and software to control, navigate, and safely drive the vehicle. AVs promise to improve our transportation system's efficiency, reduce collisions and traffic congestion, and improve mobility for our growing population. As the world continues to move toward a more efficient transportation system driven by AVs, there is an increasing demand for new technologies that guarantee AVs' efficient and safe operation. Trust in AVs hinges on the reliability of autonomy, including the crucial task of positioning, which should be accurately provided at high precision everywhere for all environments.This research will enable the development and deployment of robust positioning and mapping systems for AVs at a decimeter level of accuracy with high integrity. Our proposed solution will sustain this performance seamlessly everywhere in all operating conditions. With our industry partner, Micro Engineering Tech, we will introduce a new paradigm for the positioning and mapping of AVs. It will expand the present technologies' capabilities and enable the processing of a broader range of complementary sensors and systems on a single platform. This research will revolutionize AV multi-sensor positioning and mapping. We will develop unique machine learning methods to manage and coordinate the heterogeneous set of AV sensors/systems for efficient and safe autonomous driving. This research will significantly impact the acceptance of and trust in AVs by enhancing safety and reliability and decreasing the failure rate in degraded environments. It will also help achieve ubiquitous positioning technology and improve Canada's position in the AV services sector. Moreover, the training of highly qualified personnel (HQP) is an essential component of this research program. A diverse group of 10 talented HQP will be involved to contribute towards advancing Canada's AV technologies.