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

2025-12-14

AI Summary

Safety-critical interactions between autonomous vehicles (AVs) and bicyclists—vulnerable road users—in smart cities pose significant challenges due to high relative speeds and inherent motion uncertainty, rendering conventional collision-avoidance methods insufficiently robust. Method: This paper proposes a novel high-order Control Lyapunov–Barrier Function–Quadratic Programming (CLF-HOCBF-QP) cooperative control framework. It unifies high-order Control Lyapunov Functions (to ensure trajectory tracking stability) and high-order Control Barrier Functions (to explicitly encode bicyclist motion predictability and traffic regulations) within a single real-time quadratic programming optimization. Contribution/Results: Evaluated on three realistic, fatality-based scenarios reconstructed from the U.S. Fatality Analysis Reporting System (FARS), the framework achieves 100% collision-free navigation while reducing trajectory tracking error by 42%. It provides rigorous, simultaneous guarantees of closed-loop stability and real-time multi-obstacle avoidance, demonstrating effectiveness, robustness, and computational efficiency in complex, dynamic urban traffic.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High Order Control Lyapunov Function High Order Control Barrier Function Quadratic Programming (HOCLF HOCBF QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF HOCBF QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.

Problem

• Develops a control framework for autonomous vehicles to ensure bicyclist safety
• Addresses collision avoidance in complex traffic using stability and safety constraints
• Evaluates controller performance in simulated bicyclist crash scenarios for robustness

Innovation

• High Order Control Lyapunov Function ensures vehicle trajectory stability
• High Order Control Barrier Function prevents collisions with obstacles
• Quadratic Programming optimizes control for safety and stability