Bridge Zhao
Assistant Professor of Electrical and Computer Engineering Loyola Marymount University
- Los Angeles CA
Develops human-centered AI for safer, more sustainable mobility—from human behavior to intelligent vehicles and transportation networks.
Biography
His work combines machine learning, computer vision, robotics, control, optimization, digital twins, and multimodal data analytics. Using naturalistic driving data—including vehicle telemetry, video, and GPS—alongside human-in-the-loop simulation and field experiments, he studies driver behavior and human–automation interaction. Applications include personalized advanced driver-assistance systems (ADAS), driver intention and trajectory prediction, automation disengagement analysis, cooperative automated driving, and network-level traffic management. His research spans scales from individual behavior and vehicle trajectories to multi-vehicle systems, roadway networks, and system-level operations.
Before joining LMU, Dr. Zhao was a Postdoctoral Associate at the MIT AgeLab and Center for Transportation & Logistics and worked with the Advanced Vehicle Technology consortium, where he studied real-world use of ADAS and automated-driving technologies. He has also conducted research at Honda Research Institute and Toyota Motor North America InfoTech Labs and contributed to government- and industry-funded projects on connected and automated vehicles, intelligent transportation systems, and safety-critical AI.
Dr. Zhao received his Ph.D. in Electrical and Computer Engineering from the University of California, Riverside, in 2023, his M.S. from The Ohio State University, and his B.S. from the University of Electronic Science and Technology of China. He serves as an Associate Editor of the IEEE Open Journal of Intelligent Transportation Systems.
Education
University of California, Riverside
Ph.D.
Electrical and Computer Engineering
2023
The Ohio State University
Master of Science
Electrical and Computer Engineering
2017
Social
Areas of Expertise
Industry Expertise
Patents
Personalized vehicle operation for autonomous driving with inverse reinforcement learning
17572486
Systems and methods are provided for implementing personalized adaptive cruise control techniques in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with one embodiment, a method comprises receiving first vehicle operating data and associated first environmental data of a plurality of vehicles; classifying the first vehicle operating data and the first environmental data into a plurality of driver type classifications; training a control policy model for each driver type classification based on the first vehicle operating data and the first environmental data; receiving a real-time classification of a target vehicle based on second vehicle operating data and associated second environmental data of the target vehicle; and output a trained control policy model the to target vehicle based on the real-time classification of the vehicle, wherein the target vehicle is controlled according to the trained control policy model.
Personalized adaptive cruise control based on steady-state operation
17578330
A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.
Systems and methods for predicting driver visual impairment with artificial intelligence
12071141
According to various embodiments of the disclosed technology provide for systems and methods for predictive assessment of driver perception abilities based on driving behavior personalized to the driver.
In accordance with some embodiments, a method is provided that comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.
In another aspect, a system is provided that comprises a memory and one or more processors that are configured to execute machine readable instructions stored in the memory for performing a method. The method comprises receiving historical vehicle operating data and associated historical gaze data of a driver operating a vehicle; learning a reward function based on application of inverse reinforcement learning (IRL) to the historical vehicle operating data and the historical environmental; calculating a cumulative reward from the reward function based on real-time vehicle operating data and associated real-time gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on the cumulative reward.
Other features and aspects of the disclosed technology will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the features in accordance with embodiments of the disclosed technology. The summary is not intended to limit the scope of any inventions described herein, which are defined solely by the claims attached hereto.
Method and system for personalized car following with transformers
12083883
In an embodiment, a method may include determining a vectorized representation of a position or road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.
In another embodiment, a remote computing device may include a controller. The controller may determine a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle. The controller may input the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network. The controller may predict one or more road agent trajectories at one or more future time steps based on an output of the transformer network. The controller may predict an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps. The controller may cause the vehicle to perform the predicted acceleration at the one or more future time steps.
A system may include a vehicle including one or more vehicle sensors and a remote computing device including a controller. The vehicle sensors may collect sensor data including positions of road agents at a plurality of time steps and road geometry. The controller of the remote computing device may determine a vectorized representation of the positions of the road agents and the road geometry based on the sensor data from the vehicle. The controller may input the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network. The controller may predict one or more road agent trajectories at one or more future time steps based on an output of the transformer network. The controller may predict an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps...
