Here at the UNT College of Engineering, our research in connected ground vehicles spans across a wide array of disciplines and applications. With 8 faculty members leading graduate and undergraduate students, our research intersects the disciplines of electrical, mechanical, engineering technology, materials science, and both computer science and computer engineering.

Connected Autonomous Vehicles (CAV)

  • Accurate pedestrian and cyclist detection using 2D and 3D data fusion
  • Finding blurry vehicles using Generative Adversarial Network
  • Designing efficient deep learning models for autonomous vehicles
  • Cooperative perception with data sharing between cars
  • Edge computing for traffic monitoring
Golf cart with installed sensors
Car and surrounding objects


Edge Computing

Diagram showing edge computing structure

  • Edge servers are much closer to cars
  • Low latency for data movement, high responsiveness
  • More computing capability than cars
  • Local traffic management
    Traffic monitoring & update, accident analysis, intersection safety


Finding Blurry Vehicles

Using enhanced super resolution Generative Adversarial Network (SRGAN)

blurry vehicle detection

Blurry vehicle 1 - before detection
Blurry vehicle 2 - processing
Blurry vehicle 3 - detected


Cooperative Perception

LiDAR based data fusion for connected and autonomous vehiclesLiDAR based data fusion for connected and autonomous vehicles

Goals of data fusion

  • Enhances the accuracy of object detection for autonomous vehicles
  • Provides real-time information for traffic management and traffic control
  • Provides anonymity of private information within the sensing range
  • Increases system's reliability in case of sensor failures

Object detection on fused frames

Vehicle detection 1
Vehicle #1
Vehicle detection 2
Vehicle #2
Vehicle detection - fused result
Fused result

Occluded Road Sign Detection

LiDAR-Assisted Detection

Four different traffic signs detected

Transfer learning based solution

Stop sign as an example

Experiment results

table - mAP values of two approches

table - approche vs size

Bar chart - occluded vs no-cluded


Low-Power Hardware for Real-Time Edge ML

A fully programmable architecture based on the open RISCV ISA, leveraging:

  • Deep Quantization
  • Feature sparsity
  • Vectorized Execution
  • Memory-side scatter-gather

ML ISA feedback

Feedback-driven Framework for ML ISA

Solution comparison circle chart - on performance, programability, power and area

Vehicle Location Privacy Protection

  • Vehicle traffic-aware location privacy threat models
  • Location obfuscation mechanisms to protect against traffic-aware inference attacks
  • Time-efficient obfuscation generation algorithms

Block diagram of Obfuscation framework and threat model


Person and Object Detection


Images with color squares on detected objects.


Smart Microgrids for Autonomous Vehicle Power

Block diagram of low voltage smart microgrids

  • Bottom-up power electronics microgrid solution
  • Smart energy utilization technology
  • Scalable, cost-effective and environmental-friendly design
  • Hybrid circuit breaker solution for long life time protection


Mesh Network for Swarm Robotics

  • The swarm robotics can achieve more challenging goals than what any single robot can achieve by working collaboratively.
  • Individual robots may have simple design with limited computing, power, and functional capabilities.
  • The swarm robotics network is distributed, decentralized, self-organizing, self-healing, and scalable.

Mesh network implementation

BLE mesh network implementation with ESP32
for localization and tracking applications

Swarm robotics platform

TI-RSLK MAX robotic cars as swarm robotics platform

Autonomous Ground Vehicles (AGV) Optimization and Precision Manufacturing


Frame and parts design

Design and Optimization of Automotive Structures

Finished prototype parts

Rapid Prototyping and Proof of Concept Testing

Three equipment in Digital Manufacturing Lab

Additive Manufacturing Systems


Drone Nnetwork and Communications

Aerial Communication Infrastructure for Smart Emergency Response

Drones and a disaster area

  • First drone-carried WiFi communication system
  • Long-range (up to 5 miles) and broadband (54Mbps) drone-to-drone wireless link
  • Received various awards and media coverage
  • Community-centric project with collaborators: NCTCOG, Austin, Denton, Tarrant

Networked Airborne Computing Platform

Drone with comp, ctrl and comm three units

  • Smart platform of modular design and full functionality
  • Flexibility and extensibility for new development
  • Friendly application development capability

3D Communication System

drones with different task

  • Robust communication between drones for coordination
  • Fast data collection and information fusion with drone networks and on-ground sensors
  • Broadband networks including air-to-air and air-to-ground to enable real-time information exchange
  • Seamless integration of drone networks with Internet of Things (IoT)


SIMON: Semantic Inference Model for Security in Cyber Physical Systems Using Ontologies

Simon utilizes ontologies and extended NIST CPS framework to identify and enumerate cyber threats that affect a CPS system of interest.

Diagram of SIMON framework

Lubricants and Coatings

Nanocarbon: Solid Lubricants

Line chart showing coefficient of friction vs cycles

  • Particular for the applied case
  • Minimizes friction and wear
  • Replaces toxic and carcinogenic materials
  • Simplifies deposition procedures
  • Reduces waste


Comparison between Ov oil and Castor oil

  • Naturally high viscosity
  • High thermal stability
  • High oxidation resistance
  • Friction and wear reduction


Nanocarbon: Anticorrosion Coatings

Monitoring pitting corrosion propagation dynamics through the defects in protective coatings

Line chart - resistance vs time

Quartz Crystal Microbalance is used for monitoring the dynamics

images of iron, carbon, chrome

High degree of the corrosion in the defective sites is observed

Three images each with a line chart below

Correlation between mass gain/loss as function of defects concentration is established


Nanoporous Ceramics: Antireflective Coatings

  • Aluminum oxide, zinc oxide, titanium oxide
  • High temperature stability, non-reactivity with UV light, alpha and gamma radiation
Two coating images each with a line chart below
Line chart - reflectance vs Wavelength


Nanoporous Ceramics: Antifogging Coatings


Three different layers of coatings

Contact angle of water droplet at the surface of plain glass and glass with single layer and graded index ARCs

Glasses with and without ARC


Nanoporous Ceramics Composites: Antiwear Coatings

Line chart - coefficient of friction vs cycles

Coefficient of friction is decreasing with alumina layers

3D graph shows P2, P4VP and Al2O3

Reinforcement of polymer composites