High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

Ensuring the safety of vulnerable road users is a critical challenge in the development of advanced autonomous driving systems. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and manageable. Compared to pedestrians, vehicles often travel at significantly higher relative speeds when interacting with bicyclists, yet bicyclist motion is generally more predictable and governed by clear traffic rules, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. By using this approach, CLFs enforce system stability, ensuring the vehicle follows a desired trajectory, while CBFs define safety boundaries, preventing collisions between the ego-vehicle and other road users. The optimal control input is then obtained by solving a QP problem, which ensures that both stability and safety constraints are satisfied. In the simulation study, several key bicyclist crash scenarios identified by the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving control strategy. Simulation results demonstrate that the HOCLF-HOCBF-QP controller can help the vehicle to perform robust, and collision-free maneuvers in the vicinity of bicyclists, highlighting its potential for improving bicyclist safety in complex traffic environment.