Pre-College autonomous vehicle and robotics program for high school students with programming experience
July 6–10, 2026
University of Delaware | ICAT Lab at STAR Campus, Newark, Del.
Curious about how autonomous vehicles think, sense and navigate the world? The Autonomous Driving Academy invites motivated high school students to explore the technologies that power modern robotics and self‑driving systems.
Designed for rising high school sophomores, juniors and seniors (ages 15+), this five‑day, hands‑on program welcomes students interested in engineering, robotics, artificial intelligence, computer science and programming who want to build on what they already know.
Want to know if this program is right for you? Share your interests and experience through our interest form.
Students at the Autonomous Driving Academy will:
- Strengthen programming skills used in autonomous systems
- Explore vehicle control, sensors and decision‑making software
- Learn from UD researchers leading cutting‑edge autonomous driving work
- Apply concepts through hands‑on projects that simulate real‑world scenarios
- Collaborate with peers while building teamwork and presentation skills
- Receive a personalized reference letter for college applications and a digital badge to share online
About the Program
Created in collaboration with UD’s Connected and Autonomous Research Laboratory (CAR Lab) and the Department of Computer and Information Sciences, this noncredit, week‑long summer program offers students an immersive introduction to autonomous vehicle technology led by researchers at the forefront of the field.
Participants work with programming tools used in the field — Python, C++ and ROS — and learn how autonomous systems integrate software, sensors and control logic to operate effectively.
Students collaborate in teams to implement and refine autonomous driving behaviors in a supported, guided environment.
Recommended background for the best experience
Students will get the most out of the program if they have:
- Prior exposure to robotics or related technical coursework
- Comfort programming in Python and some familiarity with C++
- Enthusiasm for problem‑solving and collaborative learning
Students will receive guided coding exercises and prompts; they will not be expected to code everything from scratch and may use resources such as online references or AI tools.
Getting Started
To help ensure a great experience for everyone, students are asked to share a bit about their interests and experience.
As part of the interest process, students will:
- Submit a short statement (up to 250 words) about their interest in robotics or autonomous systems
- Optionally share links to project work, such as GitHub repositories or portfolios
Students taking robotics, honors or AP‑level courses, or who regularly code independently, are especially encouraged to participate.
Program Details
Autonomous Driving Pre-College Summer Program — Noncredit Program
LOCATION: ICAT Lab at STAR Campus, University of Delaware, Newark, Del. Housing is not provided; rooms can be reserved at the Baymont by Wyndham Newark I-95 at University of Delaware at a special group rate. At least one guest in each room must be 21 or older.
SCHEDULE: July 6-10, 2026, 9 a.m.-4 p.m., Monday-Friday
PRICE: $2,500, all materials included. Discounted price is $2,250 through April 1. Coupon code: EBIRD
REGISTRATION DEADLINE: June 1
NONCREDIT CONTACT HOURS: 3.0 CEUs | 30 noncredit contact hours
TECHNOLOGY REQUIREMENT: Students must bring their own laptop.
LUNCH: Students should bring their own lunch for the first four days of the program. Lunch is provided on the final day of the program.
Financial options available
Only one discount or scholarship award can apply per registration.
Registration by June 1 is required to order supplies.
EARLY REGISTRATION: Use coupon code EBIRD to receive a 10% discount when registering by April 1.
DISCOUNTS: A 15% discount is available for dependents of military and veterans, UD employees and UD alumni, or two or more siblings registering from the same family. For details or to receive the discount code, please email continuing-registration@udel.edu.
PARTIAL SCHOLARSHIPS: Partial need-based scholarships are available in the amount of a $625 scholarship award. Applications are accepted on a first-come, first-served basis. The application deadline is May 18.
See the Academy in Action
Get a glimpse of the hands-on learning and collaboration that define the Autonomous Driving Academy. This short video features participants engaging with cutting-edge technology, building skills and exploring real-world applications in autonomous systems.
Recommended experience
The ideal candidates will have a background in robotics and proficiency in Python with some knowledge of coding in C++. Students will be given coding exercises but will not need to code from scratch. Instead, they will receive prompts where they can use resources like Google or ChatGPT for help.
Course outline
Monday
Topics:
- Introduction to the hardware of autonomous robots
- Safety protocols
- Vehicle control fundamentals
- Sensing technologies overview
Activities:
- Assembling autonomous robot kits in groups
- Testing the robot’s control and reading sensor data
Tuesday
Topics:
- Autonomous driving system fundamentals
- Programming tools overview: ROS, Python, C++
- Key autonomous driving functions: mapping, localization, path planning
Activities:
- Installation of ROS and autonomous driving packages
Wednesday
Topics:
- Autonomous racing and parking systems
- Optimization techniques for racing, including trajectory optimization and reinforcement learning
Activities:
- Hands-on baseline implementation of autonomous racing application
- Optimization of the baseline implementation
Thursday
Topics:
- Autonomous parking systems
Activities:
- Continuing hands-on optimizing the racing system
- Hands-on autonomous parking system implementation
Friday
Competition Day
Participants will put their knowledge and skills to the test in an exciting, high-energy racing challenge. After four days of hands-on learning, they will race their autonomous vehicles on a track, showcasing their custom-built designs and problem-solving abilities. It’s not just about speed — teams will be judged on the creativity and technical execution of their systems, with the team achieving the fastest time taking home the win! This final event emphasizes real-world applications of autonomous driving technology, providing students with a unique opportunity to see their hard work come to life in a dynamic and competitive environment.
Learner outcomes
Participants in this program will:
- Develop skills in programming tools such as ROS, Python and C++, essential for implementing and troubleshooting autonomous driving algorithms.
- Implement and enhance autonomous parking and racing applications, focusing on performance optimization and efficiency.
- Apply theoretical knowledge in practical, hands-on projects that simulate real-world autonomous driving scenarios.
- Work effectively in teams to design, build and improve autonomous driving systems, fostering a collaborative learning environment.
- Prepare and deliver technical presentations showcasing project outcomes and innovations to an audience, honing communication skills.
- Gain insights into potential career paths in the rapidly growing autonomous driving and robotics sectors.
- Develop critical thinking and problem-solving abilities in a cutting-edge technological context.
- Enhance teamwork, leadership and communication skills through collaborative group projects.
Instructional team
Weisong Shi is an Alumni Distinguished Professor and the chair of the Department of Computer and Information Sciences at UD who founded the Connected and Autonomous Research Laboratory (CAR Lab) at UD in December 2017. Shi is an internationally renowned expert in edge computing, autonomous driving and connected health: His pioneer paper in the field, “Edge Computing: Vision and Challenges,” has been cited more than 7,500 times.
Before joining UD, Shi was a member of the computer science faculty at Wayne State University, where he held multiple administrative roles, including associate dean for research and graduate studies in the College of Engineering and interim chair of the computer science department. From 2013 to 2015, he served as a program director for the National Science Foundation (NSF). Currently, he is the editor in chief of Institute of Electrical and Electronics Engineers (IEEE) Internet Computing Magazine and Elsevier Smart Health. He is the founding steering committee chair of three conferences: the Association for Computing Machinery (ACM)/IEEE Symposium on Edge Computing (SEC), the IEEE/ACM International Conference on Connected Health (CHASE) and the IEEE International Conference on Mobility (MOST). Additionally, he is the general chair of ACM MobiCom ’24, the flagship conference on Mobile Computing and Wireless Networking. Shi is a fellow of IEEE, a distinguished scientist of ACM and a member of the NSF Computer and Information Science and Engineering Advisory Committee and Computing Community Consortium Council.
William He is a fourth-year doctoral student in the CAR Lab. His research is primarily at the intersection of autonomous driving and cyberphysical systems, focusing on building safe and reliable machine learning and simulation environments for autonomous vehicles and autonomous mobile robots. He has collaborated with researchers from Autoware, Ford, Western Digital, Blue Halo, Leidos, the Federal Highway Administration and Oak Ridge National Laboratory.
Arpan Bhattacharjee is a third-year doctoral student in the CAR Lab. His research interests include edge computing for connected and autonomous vehicles (CAVs), software over-the-air (OTA) updates and autonomous vehicle simulation and testing.
Ren Zhong is a doctoral student in computer science, with a focus on mapping and localization for autonomous driving. His research is centered on using crowdsourced data to dynamically update maps, improving both the timeliness and accuracy of mapping systems to develop safer and more efficient autonomous transportation technologies.
