PMID- 34591760
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20211006
LR  - 20211006
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 30
DP  - 2021
TI  - Deep Inference Networks for Reliable Vehicle Lateral Position Estimation in 
      Congested Urban Environments.
PG  - 8368-8383
LID - 10.1109/TIP.2021.3115454 [doi]
AB  - Reliable estimation of vehicle lateral position plays an essential role in 
      enhancing the safety of autonomous vehicles. However, it remains a challenging 
      problem due to the frequently occurred road occlusion and the unreliability of 
      employed reference objects (e.g., lane markings, curbs, etc.). Most existing 
      works can only solve part of the problem, resulting in unsatisfactory 
      performance. This paper proposes a novel deep inference network (DINet) to 
      estimate vehicle lateral position, which can adequately address the challenges. 
      DINet integrates three deep neural network (DNN)-based components in a human-like 
      manner. A road area detection and occluding object segmentation (RADOOS) model 
      focuses on detecting road areas and segmenting occluding objects on the road. A 
      road area reconstruction (RAR) model tries to reconstruct the corrupted road area 
      to a complete one as realistic as possible, by inferring missing road regions 
      conditioned on the occluding objects segmented before. A lateral position 
      estimator (LPE) model estimates the position from the reconstructed road area. To 
      verify the effectiveness of DINet, road-test experiments were carried out in the 
      scenarios with different degrees of occlusion. The experimental results 
      demonstrate that DINet can obtain reliable and accurate (centimeter-level) 
      lateral position even in severe road occlusion.
FAU - Zheng, Zhiyong
AU  - Zheng Z
FAU - Li, Xu
AU  - Li X
FAU - Xu, Qimin
AU  - Xu Q
FAU - Song, Xiang
AU  - Song X
LA  - eng
PT  - Journal Article
DEP - 20211005
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
SB  - IM
EDAT- 2021/10/01 06:00
MHDA- 2021/10/01 06:01
CRDT- 2021/09/30 17:20
PHST- 2021/10/01 06:00 [pubmed]
PHST- 2021/10/01 06:01 [medline]
PHST- 2021/09/30 17:20 [entrez]
AID - 10.1109/TIP.2021.3115454 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2021;30:8368-8383. doi: 10.1109/TIP.2021.3115454. Epub 
      2021 Oct 5.