Brake Tire Sense-AI: An Artificial Intelligence-Based Smart Diagnostic Framework for Early Detection of Brake and Tire Failures in Commercial Trucks to Reduce Severe Highway Accidents
DOI:
https://doi.org/10.31033/IJEMR/16.4.2026.1937Keywords:
Artificial Intelligence (AI), Commercial Trucks, Brake Failure, Predictive Maintenance, Deep Learning, Sensor FusionAbstract
Background: Brake and tire defects remain major safety concerns in vehicles. Conventional inspection and fixed-threshold monitoring can identify established defects but may miss early, multivariable patterns of deterioration. Artificial intelligence (AI), multimodal sensing and edge analytics may permit earlier identification of safety-critical mechanical degradation.
Objective: To prospectively develop and validate Brake Tire Sense-AI, a multimodal AI framework for early detection of major brake and tire defects in heavy commercial trucks and for generation of interpretable component-level risk alerts.
Methods: A prospective multicenter engineering diagnostic-accuracy cohort is proposed involving 530 heavy commercial trucks operated in Pakistan and the USA over 12 months. Brake pressure, brake temperature, wheel-speed, vibration, tire pressure, tire temperature, vehicle-load, environmental and computer-vision data will be acquired through an edge gateway. AI pipeline will combine supervised classification, temporal modeling, anomaly detection and multimodal sensor fusion. Mechanical inspection and documented maintenance findings will serve as the reference standard.
Sample size: Using the Buderer diagnostic-test framework, assuming an anticipated sensitivity of 90%, 95% confidence level, precision of ±7%, and an estimated 15% prevalence of defect-positive trucks, approximately 471 evaluable trucks are required; allowing 10% incomplete or unusable follow-up gives 523, rounded to a proposed enrollment target of 530 trucks.
Statistical Analysis: Descriptive statistics will characterize trucks, routes, duty cycles and defect events. Diagnostic accuracy will be reported with 95% confidence intervals. AUROC and precision-recall curves will assess discrimination; calibration plots and Brier score will assess calibration. Vehicle-level bootstrapping and cluster-robust methods will account for repeated episodes. Comparisons with conventional threshold-based monitoring will use paired methods where appropriate. Time-to-defect after an alert will be assessed using survival models, and false-alert burden will be reported per distance traveled.
Results: Illustrative results: In the simulated 530-truck cohort, 82 trucks (15.5%) experienced at least one adjudicated major brake or tire event. In the locked test set, Brake Tire Sense-AI identified 12 of 13 events, corresponding to 92.3% sensitivity, 92.4% specificity, 98.4% negative predictive value and AUROC 0.95. The simulated operational comparison also showed fewer roadside brake/tire breakdowns and lower maintenance downtime during AI-assisted monitoring. These values are illustrative and require prospective empirical validation.
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