Development of a Multipurpose Autonomous Driving Platform – Software & AI

Context & background

CTRL Engineering develops advanced automation and control solutions for industrial applications, including motion control systems, simulation, and vision systems. As autonomous systems become increasingly relevant across industries, there is a growing interest in building robust, extensible software platforms that can handle the complexity of real-world autonomous driving scenarios.

Modern autonomous systems rely on a diverse set of sensors — cameras, LiDAR, IMUs — that generate high volumes of data requiring real-time processing and intelligent interpretation. CTRL is eager to explore how a well-architected software stack can bring together data acquisition, sensor fusion, and AI-driven decision-making into a single, scalable platform for autonomous driving research and development.

Thesis objective

The goal of this thesis is to design and develop a software architecture for a multipurpose autonomous driving platform, with a focus on reliable data acquisition, real-time processing, and intelligent decision-making. The student will build a scalable sensor data pipeline, integrate control and navigation algorithms, and develop a flexible processing platform that supports core autonomous driving functionalities such as path planning, sensor fusion, and environment perception. The system will be validated through simulation and/or real-world testing, resulting in a robust and extensible software stack.

Research deliverables

• A technical research report evaluating software architectures, frameworks, and technologies for autonomous driving platforms.
• A scalable data acquisition and processing pipeline supporting multiple sensor types (cameras, LiDAR, IMUs).
• A software platform integrating control, navigation, and environment perception algorithms.
• A validation report based on simulation and/or real-world testing of the developed system.
• (Optional) An AI-based extension covering object detection, classification, or autonomous decision-making models.

Research question examples

• “What software architecture best supports real-time, low-latency processing of high-throughput sensor data in an autonomous driving platform?”
• “How can sensor fusion techniques be effectively implemented to improve environment perception and navigation reliability?”
• “To what extent can AI-based object detection and decision-making models be integrated into a modular autonomous driving software stack?”

Student Profile

We are looking for a motivated student with a genuine interest in software engineering, embedded systems, and intelligent autonomous systems. Relevant skills and interests include:

• Strong software engineering skills (Python, C++, or similar)
• Experience or interest in embedded software and real-time systems
• Familiarity with sensor technologies such as cameras, LiDAR, or IMUs
• Interest in AI/ML, object detection, or autonomous decision-making
• Ability to work independently, handle complex systems, and communicate findings clearly

Speciality: Software Engineering, Embedded Software, AI/ML — 1 student position available.

Interested?

We would love to hear from you! Please contact the dedicated CTRL contact person for this case. We can set up a meeting to get to know each other and discuss whether your wants and needs match ours.