ArticleSensors (Basel, Switzerland)2025
Image Sensing for Motorcycle Active Safety Warning System: Using YOLO and Heuristic Weighting Mechanism.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Current-Aware Temporal Fusion with Input-Adaptive Heterogeneous Mixture-of-Experts for Video Deblurring.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents an active safety warning system for two-wheeled motorcycles that integrates YOLO v4 image recognition technology with a heuristic weighting mechanism (HWM) model to calculate risk scores and thus alert riders. The system's analytical core is based on the NVIDIA Jetson TX2 module, with a camera mounted on the left-side rearview mirror of the motorcycle. YOLO is used to identify the type of approaching vehicle and measure the distance between the vehicle and the motorcycle. Moreover, the HWM model takes inputs such as vehicle type, spacing between the motorcycle and the vehicle, motorcycle speed, and distance from the intersection to generate potential risk scores. After training, the YOLO model for vehicle recognition achieved a mean Average Precision (mAP) of 92.78% at an Intersection over Union (IoU) threshold of 0.5. Additionally, the camera mounted at a 30° angle could clearly capture vehicles approaching from the left rear side of the motorcycle, achieving the highest vehicle recognition rate. Moreover, the HWM model generates a reasonable risk score to advise the rider to decelerate when the motorcycle is traveling at high speed with a vehicle approaching from behind, thereby reducing the risk of an accident and enhancing the safety of the motorcyclist.
Indexed as
Identifiers
What Socratic 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 Socratic graph.