Real-time Mixed-Integer Quadratic Programming for Driving Behavior-Inspired Speed Bump Optimal Trajectory Planning

TL;DR

This paper introduces a real-time MIQP-based trajectory planning method for autonomous vehicles that effectively negotiates speed bumps by mimicking human driving behavior, ensuring passenger comfort and computational efficiency.

Contribution

It presents a novel MIQP formulation incorporating speed bump constraints and human-like driving behavior within a Model Predictive Control framework for urban autonomous driving.

Findings

Effective speed bump negotiation in simulations

Maintains passenger comfort during traversal

Suitable for real-time urban autonomous driving

Abstract

This paper proposes a novel methodology for trajectory planning in autonomous vehicles (AVs), addressing the complex challenge of negotiating speed bumps within a unified Mixed-Integer Quadratic Programming (MIQP) framework. By leveraging Model Predictive Control (MPC), we develop trajectories that optimize both the traversal of speed bumps and overall passenger comfort. A key contribution of this work is the formulation of speed bump handling constraints that closely emulate human driving behavior, seamlessly integrating these with broader road navigation requirements. Through extensive simulations in varied urban driving environments, we demonstrate the efficacy of our approach, highlighting its ability to ensure smooth speed transitions over speed bumps while maintaining computational efficiency suitable for real-time deployment. The method's capability to handle both static road features and dynamic constraints, alongside expert human driving, represents a significant step forward in trajectory planning for urban