SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving

被引：0
|
作者：
Aoki, Shunsuke [1 ,2 ]
Yamamoto, Issei [2 ]
Shiotsuka, Daiki [2 ]
Inoue, Yuichi [2 ]
Tokuhiro, Kento [2 ]
Miwa, Keita [2 ]
机构：
[1] Natl Inst Informat, Tokyo, Japan
[2] TURING Inc, Tokyo, Japan
来源：
2023 IEEE VEHICULAR NETWORKING CONFERENCE, VNC | 2023年
关键词：
D O I：
10.1109/VNC57357.2023.10136277
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fully autonomous driving has been widely studied and is becoming increasingly feasible. However, such autonomous driving has yet to be achieved on public roads, because of various uncertainties due to surrounding human drivers and pedestrians. In this paper, we present an end-to-end learning-based autonomous driving system named SuperDriver AI, where Deep Neural Networks (DNNs) learn the driving actions and policies from the experienced human drivers and determine the driving maneuvers to take while guaranteeing road safety. In addition, to improve robustness and interpretability, we present a slit model and a visual attention module. We build a data-collection system and emulator with real-world hardware, and we also test the SuperDriver AI system with real-world driving scenarios. Finally, we have collected 150 runs for one driving scenario in Tokyo, Japan, and have shown the demonstration of SuperDriver AI with the real-world vehicle.
引用
收藏
页码：195 / 198
页数：4
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