Infrastructure-Enabled Autonomy: An Attention Mechanism for Occlusion Handling

被引:3
作者
Dax, Victoria Magdalena [1 ]
Kochenderfer, Mykel J. [1 ]
Senanayake, Ransalu [1 ]
Ibrahim, Umair [2 ]
机构
[1] Stanford Univ, Stanford Intelligent Syst Lab, Stanford, CA 94305 USA
[2] Ford Motor Co, Dearborn, MI USA
来源
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022) | 2022年
关键词
MODEL;
D O I
10.1109/ICRA46639.2022.9812389
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Although there has been tremendous progress in autonomous driving, navigating environments and predicting the behavior of other drivers in the presence of occlusions remains challenging. Cities have started investing in infrastructure sensors that could provide information about occluded spaces. We propose a framework that integrates infrastructure-to-vehicle communication in autonomous vehicle decision making, improving operational safety and mobility in challenging environments. By framing the problem as a partially observable Markov decision process in which querying an infrastructure sensor is a data-gathering action, we reduce the computational complexity associated with sensor processing while maintaining equivalent performance compared to an omniscient actor and demonstrate the value of infrastructure communication through a series of experiments.
引用
收藏
页码:5939 / 5945
页数:7
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