提出基于确定性马尔可夫决策过程的高速汇入速度优化算法
Speed Optimization Algorithm based on Deterministic Markov Decision Process for Automated Highway Merge
- 将变道速度规划建模为确定性马尔可夫决策过程，以状态值求解最优动作序列
- 采用加加速度（jerk）作为动作变量，避免加速度突变，提升乘坐舒适性
- 支持实时运行，已在仿真与真实道路中验证，适合自动驾驶汇入场景
本研究提出一种针对自动驾驶高速汇入场景的稳健优化算法。汇入是自动驾驶中的难点，需调整自车速度以匹配前方车辆。本文将速度规划问题建模为确定性马尔可夫决策过程（deterministic Markov decision process），能够计算每个状态的价值并可靠推导出最优动作序列。为避免加速度突变，采用加加速度（jerk）作为动作变量，但该策略扩大了状态空间，因此也探索了实现实时运行的方法。通过与基于智能驾驶员模型（Intelligent Driver Model）的简单算法对比，验证了该方法的有效性，评估涵盖仿真环境和真实道路测试。
原文摘要 · Abstract (English)
This study presents a robust optimization algorithm for automated highway merge. The merging scenario is one of the challenging scenes in automated driving, because it requires adjusting ego vehicle's speed to match other vehicles before reaching the end point. Then, we model the speed planning problem as a deterministic Markov decision process. The proposed scheme is able to compute each state value of the process and reliably derive the optimal sequence of actions. In our approach, we adopt jerk as the action of the process to prevent a sudden change of acceleration. However, since this expands the state space, we also consider ways to achieve a real-time operation. We compared our scheme with a simple algorithm with the Intelligent Driver Model. We not only evaluated the scheme in a simulation environment but also conduct a real world testing.
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