Automated segmentation and activity identification for intelligent vehicular systems

智能车辆系统的自动分割和活动识别

• 批准号: 446913-2013
• 负责人: Kulic, Danica
• 金额: $ 0.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=575264
• 关键词: Automated; segmentation; activity; identification; intelligent

Vehicular transportation is a key linchpin of modern infrastructure and the modern economy. Improving our understanding of driver behaviour and situational awareness in traffic conditions can lead to improvements in driver safety, fuel usage and decrease costs. Current systems for analysis of driving behaviour rely on custom in-vehicle sensing systems, which limit the scope to a small subset of vehicles. The aim of this proposal is to develop algorithms for driver behaviour analysis using both on-board and mobile sensors such as smartphones. The proposal would develop algorithms for both off-line and on-line segmentation and identification of driving behaviour from mobile device data. Off-line analysis can be employed for applications such as usage-based insurance, driver training and fleet intelligence. On-line analysis would open new possibilities for interaction with the user, providing a more intuitive and convenient interface, and enabling immediate and interactive feedback.

车辆运输是现代基础设施和现代经济的关键。改善我们 对驾驶员行为和交通状况的了解可以改善 驾驶员安全、燃料使用和降低成本。当前用于分析驾驶行为的系统依赖于自定义 车载传感系统，将范围限制在一小部分车辆上。这项建议的目的是 使用车载和智能手机等移动的传感器开发驾驶员行为分析算法。 该提案将开发离线和在线分割和识别驾驶的算法 来自移动终端数据的行为。离线分析可以用于诸如基于使用的应用。 保险、驾驶员培训和车队情报。联机分析将为相互作用开辟新的可能性 与用户，提供更直观和方便的界面，并使即时和互动 反馈