Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI
| 项目名称： | Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI |
| 摘要： | The perception phase, the weak link in the driving task, has been identified as the key cause of most autonomous vehicle (AV) accidents. This has been attributed to the relative infancy of computer vision (CV), the key technology in perception. Deep learning (DL) approaches have been used widely in computer vision applications, from object detection to semantic understanding, but are generally considered as black boxes due to their lack of interpretability which exacerbates user distrust and hinders their deployment in autonomous driving. It has been argued that explainable AI (XAI), an emerging concept in contemporary computer science literature where model outputs can be understood by humans, offers an opportunity to address this issue. Thus, this research project is developing an explainable end-to-end autonomous driving system as an improvement to existing autonomous driving systems. To do this, the team is using a state-of-the-art self-attention-based model that generates driving actions with corresponding explanations using visual features from images from onboard cameras. The model will imitate human peripheral vision by performing soft attention over the images’ global features. |
| 状态： | Active |
| 资金： | 240000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Center for Connected and Automated Transportation |
| 项目负责人： | Tucker-Thomas, Dawn<==>Bezzina, Debra |
| 执行机构： | Purdue University, Lyles School of Civil Engineering |
| 主要研究人员： | Chen, Sikai；Labi, Samuel |
| 开始时间： | 20220401 |
| 预计完成日期： | 20230331 |
| 主题领域： | Data and Information Technology；Highways；Operations and Traffic Management；Vehicles and Equipment |
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