loading page

Reliable autonomous driving environment model with unified state-extended boundary
  • +4
  • Xinyu Jiao ,
  • Junjie Chen ,
  • Yunlong Wang ,
  • Kun Jiang ,
  • Zhong cao ,
  • Mengmeng Yang ,
  • Diange Yang
Xinyu Jiao
Tsinghua University

Corresponding Author:[email protected]

Author Profile
Junjie Chen
Author Profile
Yunlong Wang
Author Profile
Kun Jiang
Author Profile
Zhong cao
Author Profile
Mengmeng Yang
Author Profile
Diange Yang
Author Profile


From the early stage of robotic applications to current autonomous driving technologies, environment modeling has been acting as the middleware of connecting perception and decision layers. In robotic applications, space-oriented models (e.g., grid map, drivable area) are widely applied to faithfully reflect the space occupation. With the development of autonomous driving, highly dynamic and complex road environment brings rising need of understanding the type and motion status of objects, thus element list became the mainstream of environment model. However, along comes the reliablity problem caused by missed detection and irregular objects, which is still inevitable despite the detection accuracy improvement.
In view of this, we provide a new view of driving environment with the proposed unified state-extended boundary (USEB), aiming to improve the reliablity of element-oriented model. For driving decision requirements, different types of elements are consistently converted into driving constraints. Semantics and dynamics are expressed as status of drivable area boundary, making it possible to merge space occupation to improve reliability against missed detection and irregular objects. Evaluation of USEB is carried out on nuScenes dataset. Comparative results show that the proposed USEB could cover the required information of driving decision, whereas achieving higher reliability than commonly applied element-oriented model.
Jan 2023Published in IEEE Transactions on Intelligent Transportation Systems volume 24 issue 1 on pages 516-527. 10.1109/TITS.2022.3216774