Real time complex scene segmentation and prediction in urban environments

城市环境中的实时复杂场景分割和预测

• 批准号: 2327211
• 金额: --
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2327211
• 关键词: Real; time; complex; scene; segmentation

Real time complex scene segmentation and prediction in urban environments The project will be looking atspatiotemporal sematic segmentation and prediction of actions of other road users in the context of SAE level 4-5autonomous vehicles. But specifically, it will focus on inner city/urban driving. It will focus on the computer vision tools forsegmentation and reasoning about scene content and motion with integration into ROS and testing on our autonomoustestbed.

城市环境中的真实的时间复杂场景分割和预测该项目将在SAE 4- 5级自动驾驶汽车的背景下研究时空语义分割和其他道路使用者的行为预测。但具体来说，它将侧重于内城/城市驾驶。它将集中在计算机视觉工具分割和推理的场景内容和运动与集成到ROS和测试我们的实验台。