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

基于机器学习的自动驾驶汽车多传感器定位和建图

• 批准号: 560898-2020
• 负责人: Noureldin, AboelmagdM
• 金额: $ 6.12万
• 依托单位: Royal Military College of Canada
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=751924
• 关键词: Machine; Learning; based; Multi; Sensor

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.

自动驾驶汽车（AV）是可以在有限或无人干预的情况下行驶的汽车，公共汽车或卡车。它们结合了传感器，感知系统和软件，以控制，导航和安全驾驶车辆。 AVS承诺提高我们的运输系统的效率，降低碰撞和交通拥堵，并改善我们不断增长的人口的流动性。随着世界继续朝着由AV驱动的更有效的运输系统迈进，人们对新技术的需求不断增长，以确保AVS的高效和安全操作。对AVS取决于自治的可靠性的信任，包括定位的关键任务，应在所有环境中精确地精确地提供。这项研究将使AVS在DECIMETER准确性高的准确性水平上为AVS的强大定位和映射系统的开发和部署。我们提出的解决方案将在所有操作条件下无缝地维持这种性能。借助我们的行业合作伙伴Micro Engineering Tech，我们将引入一个新的范式，以定位和映射AV。它将扩大当前技术的功能，并在单个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，以管理和协调一组AV传感器/系统集，以高效且安全的自动驾驶。这项研究将通过提高安全性和可靠性并降低降级环境中的故障​​率来显着影响对AV的接受和信任。它还将有助于实现普遍存在的定位技术，并提高加拿大在AV服务领域的地位。此外，对高素质人员（HQP）的培训是该研究计划的重要组成部分。将涉及10个有才华的HQP组成的多样化小组，为推进加拿大的AV技术做出贡献。