DeepScenario: City-Scale Scenario Generation for Automated Driving System Testing & Evaluation
| 项目名称： | DeepScenario: City-Scale Scenario Generation for Automated Driving System Testing & Evaluation |
| 摘要： | In this project, we will build a city-scale scenario generation and simulation platform for ADS testing and evaluation. Under different routes and environmental conditions, the simulation platform can generate testing scenarios dynamically along the route to interact with the CAV and systematically evaluate its performance. Meanwhile, a set of corner cases regarding vulnerable road users (VRUs) will be identified and added to the generated scenario library. We will leverage and extend our existing work in scenario generation and integrate it with VISSIM, CARLA, and Autoware. The platform will also be integrated with the augmented reality testing environment to enable the testing of real CAVs. |
| 状态： | Active |
| 资金： | $498,083 |
| 资助组织： | Department of Transportation |
| 项目负责人： | Tucker-Thomas, Dawn |
| 执行机构： | University of Michigan Transportation Research Institute<==>University of Michigan, Ann Arbor |
| 开始时间： | 20200301 |
| 预计完成日期： | 20211231 |
| 主题领域： | Data and Information Technology；Policy；Research；Safety and Human Factors；Transportation (General)；Vehicles and Equipment |
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