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Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

文献类型：期刊论文

作者Teng, Siyu2,3; Chen, Long4,5; Ai, Yunfeng6; Zhou, Yuanye7; Xuanyuan, Zhe2; Hu, Xuemin1
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2023
卷号8期号:1页码:673-683
关键词Semantics Data models Autonomous vehicles Cameras Reinforcement learning Predictive models Robustness Autonomous driving imitation learning motion planning end-to-End driving interpretability
ISSN号2379-8858
DOI10.1109/TIV.2022.3225340
通讯作者Xuanyuan, Zhe(zhexuanyuan@uic.edu.cn)
英文摘要End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.
资助项目National Natural Science Foundation of China[62273135] ; Natural Science Foundation of Hubei Province in China[2021CFB460] ; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College[2022B1212010006] ; Guangdong Higher Education Upgrading Plan with UIC r[R0400001-22] ; Guangdong Higher Education Upgrading Plan with UIC r[R201902]
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:000965615200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province in China ; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College ; Guangdong Higher Education Upgrading Plan with UIC r
源URL[http://ir.ia.ac.cn/handle/173211/53227]  
专题多模态人工智能系统全国重点实验室
通讯作者Xuanyuan, Zhe
作者单位1.Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China
2.HKBU United Int Coll, BNU, Zhuhai 999077, Peoples R China
3.Hong Kong Baptist Univ, Kowloon, Hong Kong 999077, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Waytous Inc Qingdao, Qingdao 266109, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Malardalen Univ, S-72214 Vasteras, Sweden
推荐引用方式
GB/T 7714		 Teng, Siyu,Chen, Long,Ai, Yunfeng,et al. Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(1):673-683.
APA Teng, Siyu,Chen, Long,Ai, Yunfeng,Zhou, Yuanye,Xuanyuan, Zhe,&Hu, Xuemin.(2023).Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(1),673-683.
MLA Teng, Siyu,et al."Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.1(2023):673-683.

入库方式： OAI收割

来源：自动化研究所

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