用MIQP建模模拟人类驾驶，实现实时舒适过减速带轨迹规划。
Real-time Mixed-Integer Quadratic Programming for Driving Behavior-Inspired Speed Bump Optimal Trajectory Planning
- 基于MPC的MIQP框架统一优化过减速带与乘客舒适性
- 仿真显示可平滑过渡减速带，计算效率满足实时需求
- 适合城市自动驾驶中需模仿人驾的场景
本文提出一种面向自动驾驶车辆的新型轨迹规划方法，通过统一的混合整数二次规划（MIQP）框架解决在复杂城市环境中穿越减速带的挑战。结合模型预测控制（MPC），所提方法在优化减速带通过过程的同时兼顾整体乘员舒适性。关键贡献在于构建了贴近人类驾驶行为的减速带处理约束，并将其无缝融入更广泛的道路导航需求中。在多种城市驾驶环境下的大量仿真验证表明，该方法能实现减速带处平滑的速度过渡，同时保持适用于实时部署的计算效率。该方法在处理静态道路特征与动态约束的同时融合专家级人类驾驶策略，显著推进了城市环境下自动驾驶轨迹规划的发展。
原文摘要 · Abstract (English)
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
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