A Multi-Modal Sensor Fusion Framework for Real-Time Hazard Prediction

Thesis Abstract

Real-time hazard prediction remains hindered by fragmentation across heterogeneous sensing modalities, latency in data fusion, and limited interpretability of fused outputs for decision-makers. This study addresses the problem by developing a principled multi-modal sensor fusion framework that integrates vision, LiDAR, acoustic, and environmental sensor data to produce timely hazard risk assessments with calibrated uncertainty. The aim is to enhance predictive accuracy, reduce false alarms, and provide interpretable hazard scores suitable for automated control loops and human operators. Specific objectives are (i) to design a fusion architecture that combines feature-level and decision-level fusion using a probabilistic graphical model; (ii) to implement a real-time inference engine that yields hazard predictions within a 100–200 ms latency budget; (iii) to quantify uncertainty through Bayesian fusion methods and calibration techniques; (iv) to evaluate the framework under diverse environmental conditions, including adverse weather and urban clutter; (v) to compare performance against unimodal baselines and existing fusion approaches; and (vi) to derive actionable guidelines for deployment in safety-critical environments. Methodologically, the study adopts a mixed-methods research design within an engineering and applied science context. The population comprises heterogeneous sensor suites deployed on autonomous platforms operating in urban and semi-urban corridors. A sample of 60 representative urban route segments is selected, with data collected over four months across daytime and nocturnal periods. Data collection instruments include synchronized multi-sensor rigs featuring high-definition cameras, LiDAR, acoustic arrays, thermal imaging, and environmental sensors (temperature, humidity, gas/oil vapor detectors). Ground-truth hazard events are annotated by domain experts using event logs, visual verification, and corroborating sensor cues. The framework’s core—an integrated fusion model—employs a two-tier approach (a) a deep feature extractor (CNNs for vision and LiDAR-derived representations, plus recurrent networks for temporal context) feeding into a probabilistic fusion layer based on a Bayesian network; (b) a calibrated risk estimator implemented via Monte Carlo dropout and ensemble methods to yield probabilistic hazard scores with confidence intervals. Data analysis proceeds in three strands (i) quantitative performance evaluation using metrics such as area under the ROC curve, precision-recall, calibration curves, and mean absolute error of hazard time-to-event estimates; (ii) ablation studies to determine the contribution of each modality and fusion stage; (iii) qualitative assessment of interpretability through feature importance analysis and saliency maps. The theoretical underpinnings draw on probabilistic fusion theory, with explicit reference to information theory-based fusion criteria and the risk-uncertainty balance proposed by the Theory of Probabilistic Reasoning in Sensor Networks; the framework is guided by the Activity Theory perspective on human-robot collaboration for situational awareness. Expected findings indicate that the proposed multi-modal fusion framework substantially outperforms unimodal baselines, achieving a target area under the curve improvement of at least 8–12 percentage points and a reduction in false alarms by 20–30% under challenging conditions. The probabilistic fusion component is anticipated to provide well-calibrated hazard probabilities (Brier score improvement of 0.05–0.08) and reliable uncertainty quantification, enabling safer decision-making in control loops. The framework is expected to demonstrate robust performance across varying sensor reliabilities and environmental disturbances, with qualitative demonstrations indicating enhanced interpretability through modality-wise contribution analyses. The study’s contribution to knowledge lies in (a) advancing a rigorously validated, real-time, multi-modal fusion architecture that integrates state-of-the-art perception with probabilistic reasoning for hazard prediction; (b) delivering a tested methodology for uncertainty-aware decision support in safety-critical autonomous systems; and (c) providing deployment-ready guidelines for sensor selection, synchronization, and latency management in urban environments. The main conclusion posits that integrated multi-modal fusion with calibrated probabilistic reasoning significantly enhances real-time hazard prediction while maintaining interpretability and reliability. Recommendations include extending the approach to additional modalities (e.g., radar), refining edge-computing strategies to further reduce latency, and conducting field trials in live traffic environments to assess long-term robustness and operational trust.