Robust Automatic Detection of Traffic Activity from Vehicle Pespectives
| 项目名称： | Robust Automatic Detection of Traffic Activity from Vehicle Pespectives |
| 摘要： | The accurate detection and prediction of actions by multiple traffic participants such as pedestrians, vehicles, cyclists and others is a critical prerequisite for enabling self driving vehicles to make autonomous decisions. Current approaches to teach an autonomous vehicle how to drive use reinforcement learning which is essentially relies on already collected situations as examples relying purely on visual similarity without any understanding of the semantics of the situation and therefore no ability to reason about other similar situations that may have different appearance. This can be overcome by methods that provide situation awareness to the vehicle. The idea is to enable semantically meaningful representations of road scenarios which include the physical layout of the scene, the various participants prior and current activities. The ability to abstract this semantic representation and apply it to multiple scenes that are conceptually similar allows much more robust decision-making strategies by autonomous vehicles. Essentially this allows endowing autonomous vehicles with a reasoning process. |
| 状态： | Active |
| 资金： | 146000 |
| 资助组织： | Carnegie Mellon University；Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Carnegie Mellon University |
| 项目负责人： | Kline, Robin |
| 执行机构： | Carnegie Mellon University |
| 主要研究人员： | Hauptmann, Alex |
| 开始时间： | 20220701 |
| 预计完成日期： | 20230630 |
| 主题领域： | Data and Information Technology；Highways；Vehicles and Equipment |
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