Development, Implementation and Experimental Assessment of Path-Following Controllers on a 1:5 Scale Vehicle Testbed

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing.