Evidence map›Paper›PMID 35746138›Full record

ArticleSensors (Basel, Switzerland)2022

In-Cabin Monitoring System for Autonomous Vehicles.

Ashutosh Mishra, Sangho Lee, Dohyun Kim, Shiho Kim

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

4 authors.

Ashutosh MishraSchool of Integrated Technology, Yonsei University, Incheon 21983, Korea.ORCID 0000-0001-8579-5583
Sangho LeeSchool of Integrated Technology, Yonsei University, Incheon 21983, Korea.
Dohyun KimSchool of Integrated Technology, Yonsei University, Incheon 21983, Korea.
Shiho KimSchool of Integrated Technology, Yonsei University, Incheon 21983, Korea.ORCID 0000-0001-9935-1721

Funding

Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-00966, Development of AI processor with a Deep Reinforcement Learning Accelerator adaptable to Dynamic Environment) Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-00966, Development of AI processor with a Deep Reinforcement Learning Accelerator adaptable to Dynamic Environment)
6 · The paper itself

Abstract

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

Indexed as

Artificial IntelligenceAutomobile DrivingAutonomous Vehiclesartificial intelligencehigh-level autonomous drivingin-cabin monitoringintelligent camerairregular situations

Identifiers

PMID35746138
PMCPMC9227214

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.