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Zhouqiao (Bridge) Zhao

Zhouqiao (Bridge) Zhao

Postdoctoral Associate • MIT Center for Transportation & Logistics • AgeLab

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About Me

Welcome to my digital realm! My name is Zhouqiao Zhao (赵周桥), and I go by Bridge. I'm a postdoctoral researcher at MIT AgeLab at MIT's Center for Transportation & Logistics (CTL). My research is supported by the MIT Advanced Vehicle Technology Consortium (AVT), where I explore the fascinating intersection of human-centered artificial intelligence, vehicle automation, and transportation systems.

Before joining MIT, I completed my Ph.D. in Electrical and Computer Engineering at the University of California, Riverside (UCR), under supervised by Dr. Matthew Barth and Dr. Guoyuan Wu. I was also a part of the Transportation Systems Research Group at Center for Environmental Research and Technology (CE-CERT). My research at UCR focused on Connected and Automated Vehicles (CAV) in the mixed-traffic environment, cooperation in the inteligent transportation system at different scales, and Digital Twin [Dissertation].

I also worked as a research intern at the Toyota Motor North America InfoTech Labs under the Digital Twin Roadmap, and Honda Research Institute in Driver Behavior Modeling. I am an author of 30+ refereed papers and patent applications. I'm passionate about creating solutions that bridge the gap between cutting-edge technology and practical applications that benefit society with human in the center of the loop [CV].

Beyond academia, I'm a creative technologist who enjoys capturing the world through photography and expressing ideas through sound design and music composition. I'm intersted in synthesizer design in both software and hardware. With my Electrical Engineering background, I'm always looking for new ways to create sound that is dynamic and interactive.

My Research

Vision

I believe the future of transportation lies in human-centered AI—automation that adapts to people, infrastructure, and diverse real-world contexts rather than replacing them. Roads in the coming decades will remain mixed environments, with human drivers, connected and automated vehicles (CAVs), and advanced driver-assistance systems (ADAS) sharing space.

My research centers on ensuring automation works with humans by:

  • Embedding human behavior into automation design so systems anticipate and adapt to real-world driving patterns.
  • AI models within infrastructure frameworks like cooperative driving automation (CDA) networks, digital twins, and adaptable intelligent transportation systems (ITS).
  • Building bridges between data, models, and systems—turning real-world behavior into predictive insights, and predictive insights into safer, more efficient mobility.

The goal: automation that’s safe, efficient, and sustainable, while being trusted and embraced by the people who use it.

Roadmap & Framework

Human-Centered AI Research Framework

Human-Centered AI Framework for Intelligent Transportation Systems

Transportation systems form the backbone of modern society, yet automation is often misconceived as minimizing human involvement. In reality, automated systems alter rather than eliminate human roles. My research develops a human-centered AI system that holistically integrates human behavior into automation design, ensuring Intelligent Transportation Systems are responsive to real-time needs and resilient within mixed-traffic environments.

Core Modules:

Sensing & Evaluation

Context-aware and multi-modal data engine for collection, simulation, and scenario synthesis

Behavior Modeling

Machine learning and explainable AI methods for understanding human behavior

System Applications

Optimized automation and mobility solutions for real-world deployment

Focus Areas

Connected & Automated Vehicles

I explore cooperation in ITS at all scales:

  • Macro – ride-sharing optimization, multi-vehicle routing, and coordinated dispatch for mixed fleets.
  • Meso – cooperative trajectory planning, eco-ramp merging, and formation control using CDA frameworks.
  • Micro – driver intention prediction, personalized adaptive cruise control, and cooperative lane merging. I also integrate vehicle-to-infrastructure (V2I) communication and infrastructure-side perception using roadside perception units (RSPUs). These systems enable real-time environment sensing, data sharing, and coordinated decision-making between vehicles and infrastructure.
Cooperative Driving Automation (CDA) V2I & Roadside Perception Multi-Scale Coordination (Macro/Meso/Micro) Mixed-Traffic Optimization

Human-Centered AI

I develop personalized and explainable AI models for transportation safety and automation. This includes:

  • Context-aware modeling of the driver–vehicle–environment triad using Graph Neural Networks (GNNs) and multi-modal large language models (MLLMs).
  • Predicting and interpreting driver responses to safety systems like Forward Collision Warnings (FCW), ensuring systems align with driver expectations.
  • Designing machine learning pipelines that balance performance with explainability, so stakeholders understand not just what the AI predicts, but why.
Driver–Vehicle–Environment Modeling Graph Neural Networks (GNN) & MLLM Explainable AI (XAI) in Transportation Personalized Safety Systems

Digital Twin Technologies

Digital twins provide a virtual mirror of real-world transportation systems, enabling scenario testing, predictive analytics, and real-time decision support. My work focuses on:

  • Building high-fidelity digital twins for ITS and vehicle automation, integrating data from naturalistic driving studies, roadside sensing, and simulations.
  • Using these twins to test safety systems, optimize traffic flow, and evaluate cooperative strategies before real-world deployment.
  • Supporting resilient infrastructure planning by simulating the impact of new technologies, policies, and mobility patterns at city and regional scales.
High-Fidelity Transportation Simulations Real-Time ITS Optimization Scenario-Based Infrastructure Planning Data-Driven Policy Testing

Publications

A Review of Personalization in Driving Behavior: Dataset, Modeling, and Validation

IEEE Transactions on Intelligent Vehicles

This paper provides a systematic review of personalization in driving behavior. It proposes a taxonomy to categorize personalized driving behaviors and surveys relevant datasets, modeling methodologies, and validation techniques. The paper emphasizes the need for intelligent vehicles to adapt to the complex and heterogeneous behaviors of human drivers to create a safe and efficient traffic environment.

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Design, Implementation, and Evaluation of an Innovative Vehicle-Powertrain Eco-Operation System for Plug-In Hybrid Electric Buses

2025 IEEE Conference on Technologies for Sustainability (SusTech)

This paper presents a Connected Eco-Bus system that co-optimizes the vehicle dynamics and powertrain controls of a plug-in hybrid electric bus (PHEB) to improve its energy efficiency. The system is evaluated through both simulation and Dynamometer-in-the-Loop testing, and the results show that it can achieve energy efficiency improvements of up to 32.4%.

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Driver Behavior in Response to Forward Collision Warnings Considering Driving Context

Human Factors and Ergonomics Society Annual Meeting (HFES)

This study investigates driver behavior in response to forward collision warnings (FCWs) in various driving contexts. The findings suggest that the severity of FCW alerts is associated with the driving environment, and that integrating more contextual information into FCW systems could enhance their performance and reduce unnecessary alerts.

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Inverse Reinforcement Learning and Gaussian Process Regression-based Real-time Framework for Personalized Adaptive Cruise Control

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

This paper proposes a real-time framework for Personalized Adaptive Cruise Control (P-ACC) that uses Inverse Reinforcement Learning (IRL) and Gaussian Process Regression (GPR) to learn a driver's car-following preferences and adapt to their real-time feedback. The proposed framework is shown to reduce driver intervention in automatic control systems by up to 70.9% in human-in-the-loop simulation experiments.

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End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras

2023 IEEE Intelligent Vehicles Symposium (IV)

This paper presents a spatio-temporal attention-based neural network that predicts driver lane-change intention by fusing video from in-cabin and forward-facing cameras. The model, which leverages a CNN-RNN architecture with a multi-head self-attention mechanism, achieves high accuracy in real-time, providing a more proactive approach to driver assistance.

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Driver Digital Twin for Online Prediction of Personalized Lane-Change Behavior

IEEE Internet of Things Journal

This paper develops a driver digital twin (DDT) for online prediction of personalized lane-change behavior in a mixed traffic environment. The DDT is deployed on a vehicle-edge-cloud architecture and is shown to be able to recognize lane-change intention 6 seconds in advance and improve prediction accuracy by 27.8% compared to a general model.

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Bi-Level Fleet Dispatching Strategy for Battery-Electric Trucks: A Real-World Case Study

Sustainability

This paper proposes a bi-level strategy for dispatching a fleet of battery-electric trucks. The strategy uses routing zone partitioning and metaheuristic-based vehicle routing to solve large-scale dispatching problems with pickup and delivery. The proposed strategy is shown to reduce travel distance and time significantly in a real-world case study.

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Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

This paper proposes a real-time learning framework for Personalized Adaptive Cruise Control (P-ACC) that incorporates driver feedback to adapt the car-following behavior. The framework uses Inverse Reinforcement Learning (IRL) to learn a driver's preferences from historical data and then uses real-time driver feedback to update the model. The proposed method is shown to reduce driver intervention by up to 62.8% in human-in-the-loop simulation experiments.

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Robotic Competitions to Design Future Transport Systems: The Case of JRC AUTOTRAC 2020

Transportation Research Record

This paper discusses the use of robotic competitions to design and test future transport systems. It presents the case of the JRC AUTOTRAC 2020, the first European robotic traffic competition for automated miniature vehicles. The competition aimed to engage a broader community in designing and testing solutions for improving traffic flow using connected and automated vehicles (CAVs).

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Online Prediction of Lane Change with a Hierarchical Learning-Based Approach

2022 IEEE International Conference on Robotics and Automation (ICRA)

This paper proposes a hierarchical learning-based approach for online lane-change prediction in a mixed traffic environment. The method uses a Long-Short Term Memory (LSTM) network for decision prediction and Inverse Reinforcement Learning (IRL) for trajectory prediction. The proposed system is validated on a human-in-the-loop simulation platform and is shown to be able to predict lane changes 3 seconds in advance with high accuracy.

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Personalized Car Following for Autonomous Driving with Inverse Reinforcement Learning

2022 IEEE International Conference on Robotics and Automation (ICRA)

This paper proposes a Personalized Adaptive Cruise Control (P-ACC) system that learns a driver's car-following preferences from historical data using Inverse Reinforcement Learning (IRL). The P-ACC system is shown to improve the accuracy of reproducing real-world driving profiles by up to 36.5% and reduce the takeover frequency by up to 93.4% compared to the Intelligent Driver Model (IDM).

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Real-time Adaptive Background Subtraction for Traffic Scenarios at Signalized Intersections Based on Roadside Fish-eye Cameras

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

This paper proposes a hierarchical adaptive background subtraction (BS) method for detecting moving and stationary objects at signalized intersections using roadside fish-eye cameras. The proposed method is shown to outperform existing BS algorithms in both simulation and real-world environments.

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Corridor-Wise Eco-Friendly Cooperative Ramp Management System for Connected and Automated Vehicles

Sustainability

This paper proposes a hierarchical, eco-friendly, cooperative ramp management system for connected and automated vehicles (CAVs). The system uses a stratified ramp metering algorithm to coordinate ramp inflow rates and a model predictive control (MPC)-based algorithm for speed control of individual CAVs. The proposed system is shown to improve mobility and energy efficiency compared to conventional ramp metering and no-control scenarios.

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Connected Vehicle-Based Advanced Detection of “Slow-Down” Events on Freeways

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

This paper proposes and investigates two real-time prediction algorithms for 'slow-down' events on freeways, based on high-resolution information from connected vehicles. The algorithms are shown to be able to predict slow-down events on average 3.51 seconds before they occur in high-density traffic.

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Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study

Sustainability

This paper proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework to assess the mobility and sustainability impacts of shared automated mobility services. A case study on a New York City network shows that the proposed DC-SAM service can significantly reduce the operating costs of shared automated vehicles (SAVs) and improve customer service.

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Vehicle Dispatching and Scheduling Algorithms for Battery Electric Heavy-Duty Truck Fleets Considering En-route Opportunity Charging

2021 IEEE Conference on Technologies for Sustainability

This paper proposes a bi-level hierarchical method to optimize the dispatch of battery-electric trucks for pickup and delivery. The method considers factors like en-route opportunity charging, time windows, and real-time traffic conditions to reduce the operational cost of the fleet.

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Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

National Center for Sustainable Transportation

This report proposes a hierarchical ramp merging system for connected and automated electric vehicles (CAEVs) that uses a centralized optimal control-based approach to smooth merging flow and improve system-wide mobility. The system is shown to improve mobility by up to 115% compared to conventional ramp metering, although this does not always translate to reduced energy consumption.

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Optimal Control-Based Eco-Ramp Merging System for Connected and Automated Vehicles

2020 IEEE Intelligent Vehicles Symposium (IV)

This paper proposes a hierarchical ramp merging system for connected and automated vehicles (CAVs) that uses a centralized optimal control-based approach to generate cooperative maneuvers and control ramp inflow rate. The system is shown to improve mobility by up to 147% and fuel savings by 47% compared to conventional ramp metering.

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Review on connected and automated vehicles based cooperative eco-driving strategies

Journal of Traffic and Transportation Engineering

This paper reviews the research progress of cooperative eco-driving strategies for connected and automated vehicles (CAVs). It analyzes the influence of vehicle, driver, traffic network, and social factors on the energy consumption of CAVs, and discusses five representative eco-driving scenarios. The paper also identifies the limitations of existing research and points out future research directions.

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Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies

SAE Technical Paper

This paper proposes a dynamometer-in-the-loop (DiL) development and testing platform for emerging mobility technologies. The platform integrates a test vehicle, chassis dynamometer, and traffic simulation to evaluate the environmental impacts of these technologies. A case study of a connected eco-operation system for a plug-in hybrid electric bus (PHEB) shows that the system can save more than 13% of fuel and reduce electricity consumption by 2%.

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Developing a Data-driven Modularized Model of a Plug-in Hybrid Electric Bus (PHEB) for Connected and Automated Vehicle Applications

2020 IEEE 23rd International Conference on Intelligent Transportation Systems

This paper presents a data-driven modularized modeling approach for a plug-in hybrid electric bus (PHEB) for connected and automated vehicle (CAV) applications. The model is composed of several modules, each representing a physical component of the PHEB, and each module is modeled using a Long Short-term Memory (LSTM) network. The model is trained and validated using data from extensive dynamometer-in-the-loop (DiL) testing.

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The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

This paper reviews the state-of-the-art of coordinated ramp control with mixed traffic conditions. It proposes a system architecture for cooperative ramp control in a mixed traffic environment and reviews the key components of the proposed system, including traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs.

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Projects

Personalized Adaptive Cruise Control (P-ACC)

Implemented inverse reinforcement learning algorithms to create personalized driving models for individual drivers, improving safety and comfort in autonomous vehicles.

Inverse Reinforcement Learning Behavioral Modeling

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Human-in-the-loop Simulation and Testing Platform

Developed a human-in-the-loop simulation platform to test and validate autonomous driving algorithms in real-world scenarios.

Simulation Testing Validation

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Cooperative Driving Automation (CDA) for Ramp Merging

Developed cooperative driving automation (CDA) algorithms for safe and efficient ramp merging in mixed traffic environments.

Cooperative Driving Automation Traffic Management

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Roadside Perception and Vehicle-to-Infrastructure (V2I) Communication

Developed roadside perception and V2I communication systems for real-time traffic monitoring and cooperative driving automation.

Roadside Perception V2I Communication

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Miniature Connected & Automated Vehicle Fleets

Developed a fleet of miniature connected and automated vehicles to simulate cooperative driving automation in different traffic scenarios. The project involved designing and building the vehicles perception, decision making, and control algorithms, developing communication protocols, and validating the system in various traffic scenarios.

Cooperative Driving Automation Robotics

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Creative Explorations

Photography Gallery

Capturing moments between experiments and explorations - from laboratory details to vast research landscapes.

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Musics & Sound Design

Electronic compositions exploring the intersection of technology and creativity, with influences from complex systems research.

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Get In Touch

I'm always interested in collaborating on research projects, discussing innovative ideas, or exploring opportunities at the intersection of AI, transportation, and creative technology.

Email | LinkedIn | Research Gate