Jonas Gillner

• This thesis presents the development, hardware-focused implementation, and evaluation of a novel, cost-effective static Multiple Input Multiple Output (MIMO) Frequency Modulated ContinuousWave (FMCW) radar sensor system for automotive Advanced Driver Assistance Systems (ADAS) applications such as Blind Spot Detection (BSD) and Lane Change Assist (LCA). The proposed prototype employs custom-optimized patch antenna arrays with static beam patterns and utilizes an innovative combination of different antenna cross-sections to achieve coarse angular field segmentation without dynamic beamforming or complex digital post-processing. The system is specifically optimized for low cost and low power consumption by performing signal acquisition, filtering, frequency analysis, and range estimation entirely on an embedded microcontroller unit. Electromagnetic simulations are employed not only to design and optimize the radio frequency structures but also to predict the expected outcomes supporting the novel static MIMO concept. The sensor’s performance was validated through range measurement accuracy tests with configurable characteristics, angular resolution characterization, and real-world application testing under varying conditions, including the influence of fabrication tolerances and beam overlap. The results demonstrate that accurate and reliable range detection can be achieved and adapted for different resolution and maximum range trade-offs, thereby supporting diverse signal processing strategies. Furthermore, the findings confirm that a directional field-ofview strategy can enable localized perception using minimal hardware, while also indicating that antenna design accuracy, beam overlap, side lobe control, and the embedded signal processing pipeline play critical roles that require further refinement to reach optimal performance. This work establishes a reproducible foundation for scalable high-frequency radar sensing and demonstrates how such a sensor system can significantly enhance automotive safety by providing an affordable solution that balances cost and sensing detail, making integration feasible for lowercost vehicles and applications that would otherwise rely on more complex and expensive sensor platforms. Finally, this system concept shows potential for deployment within roadside or intersection infrastructure to improve environmental awareness and benefit from reduced dynamic noise, thereby opening new avenues for future research and practical integration in cooperative Vehicle-to-Everything (V2X) scenarios that are highly relevant for safety in autonomous driving.