Real-time Mixed-Integer Quadratic Programming for Driving Behavior-Inspired Speed Bump Optimal Trajectory Planning
Year: 2025
Type: preprint
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
Source: arXiv (Cornell University)
Authors: Dinh, Van Nam, Phan, Van Vy, Dang, Thai Son, Phan, Van Du, Mai, The Anh, Le, Van Chuong, Ho, Sy Phuong, Duong, Dinh Tu, Ta, Hung Cuong
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7102028331
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