Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control

2026-07-14

AI Summary

This work addresses the performance-safety trade-off in autonomous racing overtaking maneuvers under nonlinear dynamics and real-time constraints by proposing a hierarchical framework. At the high level, a mixed-integer quadratic program (MIQP) determines overtaking topology, while at the low level, a nonlinear model predictive controller (MPC) formulated in Frenét coordinates generates safe trajectories using discrete-time control barrier functions (CBFs). The key innovation lies in decoupling combinatorial overtaking decisions from continuous safety-critical control and integrating reinforcement learning to adaptively tune the CBF decay parameter online, eliminating manual tuning and enabling robust adaptation to diverse track conditions. Experimental results demonstrate that the proposed approach significantly outperforms fixed-parameter strategies in both simulation and real-world tests, achieving high success rates and consistently balancing safety with performance across varied racing scenarios.

Abstract

Autonomous racing overtaking requires balancing competitive performance with safety under nonlinear vehicle dynamics and real-time constraints. Model Predictive Control (MPC) combined with Control Barrier Functions (CBFs) provides a principled mechanism for certifying forward invariance of a safe set. However, commonly used fixed-decay discrete-time CBF formulations can become overly conservative in interactive racing scenarios, limiting overtaking performance and requiring manual tuning across track conditions. This paper proposes a hierarchical overtaking framework that explicitly separates maneuver-level decision making from safety-certified trajectory control, reducing conservatism while preserving safety. A high-level Mixed-Integer Quadratic Program (MIQP) resolves the combinatorial passing-side selection problem by selecting a feasible overtaking topology, while a nonlinear Frenet-frame MPC enforces vehicle dynamics and safety through embedded discrete-time CBF constraints. This decomposition isolates the combinatorial complexity of maneuver selection from the continuous trajectory optimization. To further mitigate the sensitivity of fixed-decay barrier constraints, a reinforcement learning policy adapts the discrete-time CBF decay parameter online, enabling context-dependent modulation of safety margins without directly controlling vehicle inputs. Simulation and scaled-hardware experiments show that no single fixed decay parameter achieves uniformly strong performance across tracks, whereas the adaptive strategy attains the highest aggregate success rate and consistently strong safety--performance trade-offs without per-track tuning, improving robustness to environment variation while maintaining safety constraint satisfaction in nominal operation.

Problem

• Autonomous racing
• Safe overtaking
• Control Barrier Functions
• Real-time constraints
• Nonlinear vehicle dynamics

Innovation

• Hierarchical Optimization
• Control Barrier Functions
• Reinforcement Learning
• Model Predictive Control
• Autonomous Racing