{"id":13493,"date":"2026-07-13T10:47:31","date_gmt":"2026-07-13T14:47:31","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=13493"},"modified":"2026-07-13T10:48:48","modified_gmt":"2026-07-13T14:48:48","slug":"automating-the-unpredictable-buckeye-autodrives-random-obstacle-trajectory-generation","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2026\/07\/13\/automating-the-unpredictable-buckeye-autodrives-random-obstacle-trajectory-generation\/","title":{"rendered":"Automating the Unpredictable: Buckeye AutoDrive\u2019s Random Obstacle Trajectory Generation"},"content":{"rendered":"
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Today we are joined by students from The Ohio State University’s SAE AutoDrive II team – Buckeye AutoDrive.<\/div>\n
Representing the Center for Automotive Research (CAR)<\/a> and the Ohio State Department of Electrical and Computer Engineering (ECE), the Senior Design Capstone Program has partnered with Buckeye AutoDrive to bridge the gap between theory and practice. The Capstone Program serves as a vital proving ground where ECE students transition from the classroom to solving complex, real-world engineering challenges. As a student-led team competing in the AutoDrive Challenge II<\/a>, Buckeye AutoDrive brings together a multidisciplinary cohort of undergraduate and graduate students. Through this partnership, these two programs are working to shape the future engineering workforce.<\/div>\n
In this post, we will highlight how the Buckeyes utilize both MATLAB<\/a> and RoadRunner<\/a> to support their goal of improving autonomous driving systems. Specifically, we will focus on how they automate random obstacle trajectory placements. This breakthrough bolsters software testing robustness and pinpoints critical \u201cedge case\u201d scenarios. Below, we will outline the motivation and methodology of this development. Enjoy and Go Bucks!<\/div>\n
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Figure 1. Buckeye AutoDrive Vehicle<\/div>\n

Motivation<\/span><\/h2>\n
Simulation is the backbone of autonomous development, enabling rigorous software testing while eliminating the high costs and safety risks of physical trials. The Buckeye Simulation Team is dedicated to building an extensive collection of simulation tools that mirror the complexities of real-world driving. By prioritizing environmental diversity and data integrity, we empower the team to build safe, reliable software in a controlled, virtual environment.<\/div>\n
The random obstacle trajectory generator was completed as part of the 2026 MathWorks Simulation Challenge for the SAE AutoDrive II Competition. While the challenge focused on complex scenarios, the team challenged themselves to think outside of the box and solve emerging testing struggles.<\/div>\n
Designed with the demands of the autonomous vehicle industry in mind, this tool tackles the inherent unpredictability of the real world. By automating irregular obstacle placement, we allow developers to catch software “hiccups” in a safe, virtual space before the vehicle ever encounters them on the road.<\/div>\n

Methodology<\/span><\/h2>\n
The team developed a streamlined workflow to bridge the gap between MATLAB and RoadRunner<\/div>\n

Initializing and Software Feature Grouping<\/h3>\n
The first step in any RoadRunner project is establishing a seamless connection between the application and the project files. To streamline this, we developed an initialization Graphical User Interface (GUI) using the MATLAB App Designer application. Users declare the file paths for both the RoadRunner installation and the specific project folder. These selection tools can be seen in Figure 2 below.<\/div>\n
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Figure 2. Initial Graphical User Interface (GUI) Utilization<\/div>\n
Efficiency is key. Once the connection is live, users can choose their testing path:<\/div>\n