Cristian Bautista
I am a Ph.D. student in Electrical and Computer Engineering at The Ohio State University, affiliated with the Center for Automotive Research, advised by Prof. Qadeer Ahmed and Dr. Ekim Yurtsever. I earned my M.S. in Electrical and Computer Engineering from Ohio State advised by Prof. Wei-Lun (Harry) Chao.
I am seeking Summer 2027 internship opportunities in Autonomous Driving — Multimodal Perception, End-to-End AV Systems, and RL. Any referrals or leads would be greatly appreciated — please feel free to reach out.
Current Focus
I am exploring how agents can perceive and understand scenes based on their action space. How much of a scene must an autonomous agent understand to act effectively? How can collaborative perception improve the decisions and actions of multiple agents? My work is motivated by the belief that real-world feasibility should be a design requirement, not an afterthought.
- Collaborative and multimodal perception for autonomous driving.
- Real-world autonomous vehicle integration and evaluation.
- Learning-based decision-making for complex driving scenarios.
Featured Work
Buckeye AutoDrive
Team Captain & Mobility Innovation Leader
Full-stack L4 autonomous vehicle integration on a real vehicle platform, including multimodal perception pipelines, distributed computing, SLAM, MPC controller, and CAN communication.
Awards Mobility Innovation · MathWorks Simulation · System Safety · Static Events
When the City Teaches the Car
Label-Free 3D Perception from Infrastructure
Collaborative 3D perception for autonomous driving, using infrastructure-to-vehicle collaboration to improve vehicle-side perception without manual labels.
Projects
Learning-Based Decision-Making
A reproducible benchmark for high-level robotaxi decision-making on a map built from real-world vehicle data. A PPO policy selects lane-keeping or lane-change maneuvers, while a deterministic motion layer executes each action in Frenet coordinates.
Plum Selection System
Computer vision system for plum classification using convolutional neural networks, data augmentation, and Grad-CAM, with a real-world implementation.
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