| Title | Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving |
| Creator | |
| Date Issued | 2023 |
| Source Publication | IEEE Transactions on Intelligent Vehicles
 |
| Volume | 8Issue:1Pages:673-683 |
| Abstract | 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. |
| Keyword | Autonomous driving end-to-End driving imitation learning interpretability motion planning |
| DOI | 10.1109/TIV.2022.3225340 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85144050330 |
| Citation statistics | |
| Document Type | Journal article |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11627 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Xuanyuan，Zhe |
| Affiliation | 1.BNU-HKBU United International College,Zhuhai,519087,China 2.Hong Kong Baptist University,Kowloon,999077,Hong Kong 3.Chinese Academy of Sciences,State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Beijing,100190,China 4.Waytous Inc. Qingdao,Qingdao,266109,China 5.University of Chinese Academy of Sciences,Beijing,100049,China 6.Malardalen University,Vasteras,72214,Sweden 7.Hubei University,School of Computer Science and Information Engineering,Wuhan,430062,China |
| First Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Recommended Citation GB/T 7714 | Teng，Siyu,Chen，Long,Ai，Yunfenget 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. |
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