Embedded sensor fusion deep network for road user detection

用于道路用户检测的嵌入式传感器融合深度网络

• 批准号: 570655-2021
• 负责人: Laganière, RobertR
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 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=753058
• 关键词: Embedded; sensor; fusion; deep; network

During the past ten years, tremendous progress has been achieved in the fields of artificial intelligence, sensor technology, low-power processing units and connectivity. As a result, self-driving vehicles emerge as very promising and fruitful technology attracting a large number of technology giants, startups, as well as researches from all aroundthe world. An autonomous vehicle is one that incorporates a fully automated driving system, that can drive and navigate on the road by itself, without requiring any human intervention, and can respond to environmental situations. Perception is a critical module in any automated driving system. In this research project, we address the problem of the detection and localization of the road users (vehicles, pedestrians, cyclists, etc.) surrounding the driving environment of an intelligent vehicle.Intelligent vehicles are generally equipped with three main sensor types: cameras, lidar (or laser scanner) and radar. All these sensors are intrinsically very complementary and the strength of one can compensate for the weakness of the other. However, under challenging environmental conditions, such as adverse weather, the operationality of a given sensor might be severely impacted and the detection results may then become unreliable. To solve this issue, the performance of a perception system can be significantly improved by combining the information coming from different modalities to overcome the shortcomings of individual sensors. This research project will focus on the design, deployment, optimization and embedding of a convolutional neural network (CNN) for sensor fusion in the context of advanced driver assistance systems and autonomous vehicles.

过去十年，人工智能、传感器技术、低功耗处理单元和连接领域取得了巨大进步。因此，自动驾驶汽车成为一种非常有前途和富有成效的技术，吸引了世界各地大量的科技巨头、初创公司和研究人员。自动驾驶汽车是一种集成了全自动驾驶系统的汽车，可以在道路上自行驾驶和导航，无需任何人为干预，并且可以对环境情况做出响应。感知是任何自动驾驶系统中的关键模块。在这个研究项目中，我们解决智能车辆驾驶环境周围的道路使用者（车辆、行人、骑车人等）的检测和定位问题。智能车辆通常配备三种主要传感器类型：摄像头、激光雷达（或激光扫描仪）和雷达。所有这些传感器本质上都是互补的，其中一个传感器的优势可以弥补另一个传感器的劣势。然而，在恶劣的环境条件下，例如恶劣的天气，给定传感器的操作可能会受到严重影响，并且检测结果可能变得不可靠。为了解决这个问题，通过结合来自不同模态的信息来克服单个传感器的缺点，可以显着提高感知系统的性能。该研究项目将重点关注先进驾驶辅助系统和自动驾驶车辆中传感器融合的卷积神经网络 (CNN) 的设计、部署、优化和嵌入。