The optimal configuration of perception systems in autonomous vehicles is essential for accurate environmental sensing, precise navigation, and overall operational safety. In Formula Student Driverless (FSD) vehicles, sensor placement is particularly challenging due to the compact design constraints and the highly dynamic nature of the racing environment. This study investigates the positioning and configuration of two LIDAR sensors and a stereo camera on an FSD race car, focusing on field-of-view coverage, sensing redundancy, and sensor fusion potential. To achieve a comprehensive evaluation, measurements are conducted exclusively in a simulation environment, where field-of-view maps are generated, detection ranges are analyzed, and perception reliability is assessed under various conditions. The results provide insights into the optimal sensor arrangement that minimizes blind spots, maximizes sensing accuracy, and enhances the efficiency of the autonomous vehicle’s perception architecture.

This study presents the development and implementation of an electronically actuated steering system in a Formula Student Driverless race car, aiming to support autonomous driving capability. A DC motor with a belt-drive mechanism was integrated into the original steering rack assembly without altering its core mechanical characteristics. The research also includes a validation of the steering geometry using both physical measurements and CAD simulations. The objective of this measurement is to determine the steering angle as a function of the steering wheel input angle, ensuring that the resulting data accurately informs vehicle dynamics models such as the kinematic bicycle model. These steps form the basis for closed-loop control integration in the autonomous driving platform.