Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning

被引：1
|
作者：
Bagwe, Gaurav [1 ]
Li, Jian [2 ]
Deng, Xiaoheng [3 ]
Yuan, Xiaoyong [4 ]
Zhang, Lan [1 ]
机构：
[1] Michigan Technol Univ, Dept Elect & Comp Engn, Houghton, MI 49931 USA
[2] Univ Sci & Technol China, Sch Cyber Sci & Technol, Hefei, Peoples R China
[3] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
[4] Michigan Technol Univ, Coll Comp, Houghton, MI 49931 USA
来源：
2022 IEEE/ACM 7TH SYMPOSIUM ON EDGE COMPUTING (SEC 2022) | 2022年
关键词：
D O I：
10.1109/SEC54971.2022.00043
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The recent success of Artificial Intelligence (AI) has enabled autonomous driving with better perception capabilities. However, on-ramp merging remains one of the main challenging scenarios for reliable autonomous driving. Within the limited onboard sensing range, a merging vehicle can hardly observe and predict the main road conditions properly, restricting appropriate merging maneuvers. In this poster, we outline ongoing research ideas for reliable and autonomous on-ramp merging assisted by vehicular communications. By jointly leveraging the basic safety messages (BSM) from neighboring vehicles and the surveillance images, a merging vehicle can perform reliable driving via robust multimodal reinforcement learning. Some experimental results are provided to evaluate our idea under the Simulation of Urban MObility (SUMO) platform.
引用
收藏
页码：313 / 315
页数：3
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