Research
I develop geometry-informed computer vision and machine learning for reliable measurement from commodity sensors. Most of my dissertation builds perception and measurement for cyclist safety: detecting overtaking vehicles, estimating passing distance, and classifying vehicles from a single camera. A related line models driver behavior from vehicle kinematics. Across both, I encode physical and behavioral structure into the pipeline so results stay reliable across sensors, platforms, and sites, and the pipeline abstains when it should not be trusted.
Perception for Cyclist Safety
Calibration-Free Lateral Passing Distance Estimation
A framework that estimates how closely a vehicle passes a cyclist from 2D bounding-box geometry alone, with no camera calibration. Three features derived from the pinhole camera model absorb the unknown calibration constants when fit to a cheap ultrasonic proximity sensor as the training signal.
Key results
- 0.126 m mean absolute error (MAPE 8.2%) on 92 naturalistic passing events, leave-one-group-out cross-validation.
- External validation on the Waymo Open Dataset (re-fit with LiDAR supervision, different platform and range): R² = 0.812, MAPE 6.6%, across 11,248 detections. The physics-grounded feature design transferred; the model was retrained, not applied zero-shot.
Submitted to the International Cycling Safety Conference, 2026
Geometry-Informed Overtaking Detection from a Bicycle Camera
An automated pipeline that detects vehicle overtaking events from a single rear-facing bicycle-mounted camera, with no multi-sensor rig and no calibration. A three-stage geometric validation module (bearing-angle trend, apparent-size growth, spatial confirmation) enforces perspective-geometry rules on top of RT-DETR detection and ByteTrack tracking, rejecting detections that are physically implausible for an overtaking maneuver.
Key results
- 98.1% recall with a single false positive across 315 annotated events.
- Mean advance warning of 2.37 s before passage; 83.2% of events exceed a 1.5 s reaction-time threshold.
Injury-Risk Vehicle Body-Type Classification (Open Source)
A two-stage pipeline (RT-DETR detection plus a fine-tuned ViT-Base/16) that classifies passing vehicles into six categories defined by injury risk to cyclists (passenger car, SUV, pickup, minivan, large van, commercial truck), rather than make and model. I assembled and labeled the custom dataset and designed the taxonomy. The classifier abstains and outputs "unknown" when confidence is low, rather than silently misclassifying.
Key results
- 94% accuracy in-distribution (3,805 events; per-class F1 0.91-0.97).
- 89% accuracy on separate held-out sites with no retraining, with abstention correctly rising under distribution shift.
Driver Behavior
Aggressive Driving Identification from Vehicle Kinematics
A machine learning framework that classifies aggressive versus non-aggressive driving from longitudinal jerk and driver pedal operations. The core idea is a speed-adjusted jerk threshold, four speed segments aligned with FHWA roadway functional classes, jointly optimized with the classifier over a large grid search, because the same jerk magnitude means different things at 20 mph and 60 mph.
Key results
- 94% accuracy, AUC-ROC 0.971 (Random Forest), across 556 trips and 7 vehicle models.
- Speed-adjusted thresholds consistently beat fixed thresholds across all classifiers and feature sets.
Under review, Engineering Applications of Artificial Intelligence
Acceleration and Deceleration Profiles vs. EPA Test Cycles
A speed-segmented characterization of real-world aggressive and non-aggressive driving, benchmarked against four EPA fuel-economy test cycles. The US06 cycle approximates aggressive acceleration but substantially overstates aggressive deceleration intensity; all milder cycles also overestimate deceleration relative to naturalistic driving, with implications for powertrain calibration and fuel-economy labeling.
SAE Technical Paper 2025-01-8605
WCX SAE World Congress Experience, 2025
Earlier Work
Computational Human Performance Modeling (M.S. Thesis)
An open-source implementation of the QN-MHP (Queuing Network-Model Human Processor) cognitive architecture in Python and SimPy, modeling human multitask performance as a queuing network of information processors so that interface designs can be evaluated computationally. Validated against empirical visual-manual task data.
Autonomous Driving System for Riders with ASD
A human-centered design study for an autonomous vehicle tailored to individuals with Autism Spectrum Disorder and their caregivers, developed through stakeholder engagement, with a customized interior concept and companion application addressing safety and monitoring.