Evidence map›Paper›PMID 37521046›Full record

ArticlePatterns (New York, N.Y.)2023

Understanding common human driving semantics for autonomous vehicles.

Yingji Xia, Maosi Geng, Yong Chen, Sudan Sun, Chenlei Liao, Zheng Zhu, Zhihui Li, Washington Yotto Ochieng, Panagiotis Angeloudis, Mireille Elhajj and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 14 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 7 institutions in 3 countries.

Yingji XiaInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Maosi GengInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Yong ChenInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Sudan SunSchool of Medicine, Zhejiang University, Hangzhou 310058, China.
Chenlei LiaoInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Zheng ZhuInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Zhihui LiSchool of Transportation, Jilin University, Changchun 130022, China.
Washington Yotto OchiengDepartment of Civil and Environmental Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Panagiotis AngeloudisDepartment of Civil and Environmental Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Mireille ElhajjDepartment of Civil and Environmental Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Lei ZhangAlibaba Group, Hangzhou 310052, China.
Zhenyu ZengAlibaba Group, Hangzhou 310052, China.
Bing ZhangAlibaba Group, Hangzhou 310052, China.
Ziyou GaoSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Xiqun Michael ChenInstitute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Zhejiang University · CNAlibaba Group (China) · CNImperial College London · GBBeijing Jiaotong University · CNJilin University · CNUniversity of Illinois Urbana-Champaign · USZhejiang Provincial Institute of Communications Planning,Design & Research · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.

Indexed as

autonomous vehicledriving behavior perceptiondriving semanticselectroencephalographyhierarchical understanding abstractionhuman-machine interactionneural-informed modelneuroscience

Identifiers

PMID37521046
PMCPMC10382946
OpenAlexW4366267533

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.