SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Zhou Zhe)
 

Sökning: WFRF:(Zhou Zhe) > Hierarchical Interp...

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

Teng, Siyu (författare)
Hong Kong Baptist University, Kowloon, China
Chen, Long (författare)
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Ai, Yunfeng (författare)
University of Chinese Academy of Sciences, Beijing, China
visa fler...
Zhou, Yuanye (författare)
Mälardalens universitet,Framtidens energi
Xuanyuan, Zhe (författare)
BNU-HKBU United International College, Zhuhai, China
Hu, Xuemin (författare)
School of Computer Science and Information Engineering, Hubei University, Wuhan, China
visa färre...
 (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2023
2023
Engelska.
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 8:1, s. 673-683
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy