Congratulations to Our Newest PhD Graduate, Dr. Boyu Jiang!

The Department of Statistics at Virginia Tech is proud to announce and celebrate Dr. Boyu Jiang's successful completion of the doctoral degree requirements.

Dr. Boyu Jiang successfully defended his dissertation titled "From Detection to Reliability: Supervised Contrastive Learning and Perception Evaluation in Dynamic Conditions for Autonomous Driving" on September 8, 2026, a significant milestone representing years of rigorous work, dedication, and impactful research.

Title: From Detection to Reliability: Supervised Contrastive Learning and Perception Evaluation in Dynamic Conditions for Autonomous Driving

Abstract: 

Safe autonomous driving requires both reliably detecting rare safety-critical events (SCEs) — crashes and near-crashes — and understanding when the perception systems responsible for detection can be trusted. This dissertation addresses this progression from detection to reliability. On the detection side, this work develops representation learning frameworks grounded in Supervised Contrastive Learning (SCL). Applied to SHRP 2 Naturalistic Driving Study video data, SCL enhances intra-class cohesion and inter-class separation in the learned representation space, improving crash/near-crash/baseline classification over supervised and self-supervised benchmarks. Extending this principle to vehicle kinematics, the proposed supervised contrastive variational autoencoder (scVAE) uses a dual-encoder architecture to disentangle a label-regulated salient latent space from shared driving patterns, achieving state-of-the-art clustering and enabling scenario generation, denoising, and event prediction. On the reliability side, this dissertation introduces Perception Characteristics Distance (PCD), a metric that quantifies the maximum distance at which a perception system remains reliable under a given decision rule. PCD models the mean and variance of detection confidence as a function of distance via spline detrending and sequential variance change-point detection. Evaluated on the newly introduced SensorRainFall dataset across weather and lighting conditions, PCD reveals distance-dependent reliability characteristics that conventional static metrics overlook. Together, these contributions provide a unified statistical paradigm for both detecting safety-critical events and assessing perception reliability in dynamic conditions, supporting the development and validation of safer automated driving systems.

Dr. Boyu Jiang will join Torc Robotics as a Data Scientist.