Inferring driver behaviours, intent and risk in complex traffic scenarios

推断复杂交通场景中驾驶员的行为、意图和风险

• 批准号: DP160104081
• 负责人: Prof Eduardo Nebot
• 金额: $ 31.09万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-04-16 至 2019-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160104081
• 关键词: Inferring; driver; behaviours; intent; risk

This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.

该项目旨在开发评估驾驶过程中风险的方法。下一代汽车将配备复杂的感知和以自我为中心的信息。这将与实现协作安全的车间通信结合在一起，与智能基础设施一起使用。这项技术预计将从2017年开始在美国强制实施。该项目计划开发无监督学习算法，以推断高级司机的行为、意图和背景信息，以自动评估复杂驾驶场景下的风险水平。它计划使用在世界各地大规模部署的自然主义驾驶数据集来验证结果。这一创新可能会提高汽车安全性，促进自动驾驶汽车的部署。