Self-Driving Car Perception Platform

自动驾驶汽车感知平台

• 批准号: RTI-2017-00149
• 负责人: Barfoot, Timothy
• 金额: $ 10.88万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616571
• 关键词: Self; Driving; Car; Perception; Platform

Self-driving cars are looming on the horizon, but there is still much research to be done before they become a reality. Many of the outstanding technical challenges involve imbuing these vehicles with robust perception so that they might (i) know where they are, (ii) recognize dynamic objects, and (iii) understand the state of the world around them. While satellite-based positioning can help with the first task, it is widely accepted that on-board sensors will be necessary to fully solve these perception tasks. This project proposes to acquire a roadworthy research vehicle instrumented with several different types of sensors (cameras, laser rangefinders, radar, inertial measurement units); the vehicle will be used to gather large datasets in order to advance the perceptive capabilities of self-driving cars. While some such datasets exist (e.g., the well-known KITTI dataset prepared by co­applicant Urtasun - downloaded 10,000 times, used by 300 research groups worldwide), this is only a starting point. Toronto enjoys a wide variety of different seasonal conditions (particularly winter), making it a unique test area to push the boundaries of self-driving car perception. Gathering datasets in varying conditions is necessary to push research in this area, and execute the research aims of the applicants and their industrial/government partners. This project represents an important and new collaboration between the Dept. of Computer Science and the Faculty of Applied Science and Engineering and will surely be a high-profile mechanism to showcase the University of Toronto and Canadian robotics research.

自动驾驶汽车即将出现，但在成为现实之前仍有大量研究要做。许多突出的技术挑战涉及让这些车辆具有强大的感知能力，以便它们能够（i）知道自己在哪里，（ii）识别动态物体，以及（iii）了解周围世界的状态。虽然卫星定位可以帮助完成第一项任务，但人们普遍认为，要完全解决这些感知任务，需要机载传感器。该项目拟购买一辆适合道路行驶的研究车辆，配备多种不同类型的传感器（摄像机、激光测距仪、雷达、惯性测量装置）；该车辆将用于收集大型数据集，以提高自动驾驶汽车的感知能力。虽然存在一些这样的数据集（例如，共同申请人 Urtasun 准备的著名的 KITTI 数据集 - 下载了 10,000 次，被全球 300 个研究小组使用），但这只是一个起点。多伦多拥有多种不同的季节条件（尤其是冬季），使其成为突破自动驾驶汽车感知界限的独特测试区域。收集不同条件下的数据集对于推动该领域的研究并实现申请人及其工业/政府合作伙伴的研究目标是必要的。该项目代表了计算机科学系与应用科学与工程学院之间的重要且新的合作，必将成为展示多伦多大学和加拿大机器人研究的高调机制。