Evidence map›Paper›PMID 39771902›Full record

ArticleSensors (Basel, Switzerland)2024

Testing and Validation of the Vehicle Front Camera Verification Method Using External Stimulation.

Robin Langer, Maximilian Bauder, Ghanshyam Tukarambhai Moghariya, Michael Clemens Georg Eckert, Tibor Kubjatko, Hans-Georg Schweiger

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Robin LangerCARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.ORCID 0000-0002-5424-2609
Maximilian BauderCARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.ORCID 0000-0002-3313-2347
Ghanshyam Tukarambhai MoghariyaFaculty of Electrical Engineering and Information Technology, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
Michael Clemens Georg EckertFaculty of Electrical Engineering and Information Technology, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
Tibor KubjatkoInstitute of Forensic Research and Education, University of Zilina, 010 26 Zilina, Slovakia.ORCID 0000-0002-3286-321X
Hans-Georg SchweigerCARISSMA Institute of Electric, Connected, and Secure Mobility (C-ECOS), Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.ORCID 0000-0003-0184-1322

Funding

Federal Ministry of Education and Research 13FH7I05IASlovak Research and Development Agency APVV-23-0665
6 · The paper itself

Abstract

The perception of the vehicle's environment is crucial for automated vehicles. Therefore, environmental sensors' reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle's lifetime. There are several promising approaches for developing new test methods for vehicle environmental sensors, one of which has already been developed in our previous work. A method for testing vehicle front cameras was developed. In this work, the method is improved and applied again. Various test vehicles, including the Tesla Model 3, Volkswagen ID.3, and Volkswagen T-Cross, are stimulated by simulating driving scenarios. The stimulation is carried out via a tablet positioned before the camera. The high beam assist is used to evaluate the vehicle's reaction. It was observed whether the vehicle switched from high to low beam as expected in response to the stimulation. Although no general statement can be made, the principle of stimulation works. A vehicle reaction can be successfully induced using this method. In further test series, the influence of display brightness is examined for the first time in this work. The results show that the display brightness significantly influences the test procedure. In addition, the method is validated by stimulation with colored images. It is shown that no complex traffic simulation is necessary to trigger a vehicle reaction. In the following validation approach, the CAN data of the Tesla Model 3 is analyzed during the tests. Here, too, the assumption that the vehicle reaction is based solely on the detected brightness instead of identifying road users is confirmed. The final validation approach examines the method's applicability to other vehicles and high beam assist technologies. Although the method could not be used on the Volkswagen T-Cross due to a fault detected by the vehicle's self-diagnosis, it worked well on the Volkswagen ID.3. This vehicle has a dynamic light assist in which individual segments of the high beam are dimmed during stimulation. Although the method developed to stimulate vehicle front cameras is promising, the specific factors that trigger the vehicle responses remain to be seen. This uncertainty suggests that further research is needed better to understand the interaction of stimulation and sensor detection.

Indexed as

ADASfront camerahigh beam assistperiodic technical inspection (PTI)simulationTesla Model 3test drivestestingVolkswagen ID.3Volkswagen T-Cross

Identifiers

PMID39771902
PMCPMC11679538

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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.