ArticleSensors (Basel, Switzerland)2024
Analysis of Advanced Driver-Assistance Systems for Safe and Comfortable Driving of Motor Vehicles.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed.
- Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with Classification-Guided Adaptive Temporal Inference.Sensors (Basel, Switzerland) · 2026Article
- Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.Scientific reports · 2026Article
- A high-selectivity 24-GHz SIW-DGS-CPW bandpass filter with wide stopband rejection for automotive radar and ADAS.Scientific reports · 2026Article
- Visual Impairment and Driving in Older Adults: A Narrative Review.Current treatment options in neurology · 2026Review
- Classifying Advanced Driver Assistance System (ADAS) Activation from Multimodal Driving Data: A Real-World Study.Sensors (Basel, Switzerland) · 2025Article
- Elucidation of user autonomous driving system preference mechanisms under the extension of internal and external factors.Scientific reports · 2025Article
- A vehicle coordinate system reconstruction method for end of line calibration applications.Scientific reports · 2025Article
- Recent Advances in Vehicle Driver Health Monitoring Systems.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers' positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly.
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What OpenQuestion holds
Registered trials
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.