ArticleCyborg and bionic systems (Washington, D.C.)2025
Safety Decision-Making for Autonomous Vehicles Integrating Passenger Physiological States by fNIRS.
Article in Cyborg and bionic systems (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed.
- Personalized Classification of Scenario-Derived Operational Driver-State Classes from Non-Intrusive Wearable Signals in Real-World SAE Level 2 Automated Driving.Sensors (Basel, Switzerland) · 2026Article
- Detecting and Improving Human Cognitive State in Real-Time Using Data-Driven Adaptive Systems: A Systematic Review.Bioengineering (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, several serious traffic accidents have exposed the severity of safety issues in autonomous driving technology. Traditional decision-making methods are unable to address potential risky behaviors caused by the functional insufficiencies or machine performance limitations, and human intervention is still needed. This study proposes an intelligent safety decision-making algorithm with passengers' risk assessment by analyzing passenger physiological states online using functional near-infrared spectroscopy (fNIRS). This algorithm is developed based on twin-delayed deep deterministic policy gradient (TD3), and it can overcome the functional insufficiencies of traditional TD3 and guide TD3 using passengers' risk assessment by analyzing passenger physiological states online while confronting risky scenarios. Three experiments have been conducted in autonomous emergency braking, front vehicle cutting-in, and pedestrian crossing scenarios. The results show that the proposed algorithm demonstrates faster convergence and superior safety and comfort performance compared with traditional TD3. This study highlights the applicability of fNIRS technology in enhancing the safety and comfort of autonomous vehicles in the future.
Identifiers
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.