The utilization of driverless forklifts necessitates stringent safety measures to prevent any harm to human or material involved in their operation. This thesis addresses the critical need for collision detection algorithms for driverless forklifts, particularly in scenarios where traditional sensors are obstructed during loading and unloading processes. Instead of relying on external sensors, this research focuses on utilizing the internal sensors already present in the forklift. Signals from the forklift were collected during various driving scenarios in a controlled lab environment. Five different algorithms were developed and evaluated, providing detailed insights into their strengths and limitations. These algorithms employ a range of techniques, including physical modeling, regression modeling, residual analysis, and machine learning classification. All five algorithms demonstrate notable accuracy and reliability in collision detection. The research contributes to the advancement of collision detection technology in industrial environments, offering practical insights for safer and more productive material handling operations.