Game-Theoretic Lane-Changing Decision-Making for Autonomous Vehicles
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摘要:
针对自主式交通系统中自动驾驶车辆的换道决策难题，提出一种基于不完全信息动态博弈的换道决策模型. 首先，基于博弈论思想，明确换道意图判断、博弈换道条件以及模型求解等核心问题，并建立相应的博弈框架；为优化收益函数，构建一个综合考虑安全性、效率性和舒适性的多目标收益函数，选择合适的特征对各目标收益进行量化处理，并结合逆强化学习方法从NGSIM数据中学习权重分配，确保了决策的多维度考量. 实验结果显示：无论是在博弈成功还是失败的交通场景中，所提模型都能有效提升决策的安全性和效率；相较于传统的防御型换道模型，该模型在提高行驶效率方面表现更为优越，展现了其在自动驾驶换道决策中的实用性和优势；此外，通过SUMO仿真验证，模型在真实高速公路场景中表现出良好的安全性和通行效率，能有效适应交通流变化，保障车辆安全高效行驶，验证了其在自动驾驶换道决策中的实用价值.
Abstract:To address the lane-changing decision-making challenge of autonomous vehicles in autonomous transportation systems, a lane-changing decision model based on an incomplete-information dynamic game was proposed. First, grounded in game-theoretic principles, core issues such as lane-changing intention recognition, game-based lane-changing conditions, and model solution methods were delineated, and a corresponding game framework was established. To optimize the payoff function, a multi-objective payoff function that comprehensively considers safety, efficiency, and comfort was constructed, with appropriate features selected to quantitatively evaluate the payoff of each objective. In conjunction with inverse reinforcement learning, the weight allocation was learned from the NGSIM dataset, ensuring multi-dimensional consideration in decision-making. Experimental results indicate that the proposed model effectively enhances decision safety and efficiency in both successful and failed game traffic scenarios. Compared with traditional defensive lane-changing models, the proposed model demonstrates superior performance in improving driving efficiency, demonstrating its practicality and advantages in autonomous vehicle lane-changing decision-making. Furthermore, validation through SUMO simulation shows that the model exhibits good safety and traffic efficiency performance in realistic highway scenarios, effectively adapts to variations in traffic flow, and supports safe and efficient vehicle operation, thereby verifying its practical value in autonomous vehicle lane-changing decision-making.
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图 3 换道决策阶段轨迹提取结果
Figure 3. Trajectory extraction results of lane-changing decision-making stage
图 4 换道车辆与目标车道后车的交互范围
Figure 4. Interaction range between lane-changing vehicle and following vehicle in target lane
图 5 基于换道博弈模型的博弈过程（博弈成功工况）
Figure 5. Game process based on lane-changing game model （successful game scenario）
图 6 基于换道博弈模型的博弈过程（博弈失败工况）
Figure 6. Game process based on lane-changing game model （failed game scenario）
表 1 收益矩阵
Table 1. Payoff matrix
车辆类型 换道策略 PFV 加速 减速 LV 换道 $ \left(U_{\text{LV},11},U_{\text{PFV},11}\right) $ $ \left(U_{\text{LV},12},U_{\text{PFV},12}\right) $ 不换道 $ \left(U_{\text{LV},21},U_{\text{PFV},21}\right) $ $ \left(U_{\text{LV},22},U_{\text{PFV},22}\right) $ 
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表 2 状态集和动作集
Table 2. State set and action set
参数 状态集 动作集 相对
距离/m相对
速度/（m•s−1）本车
速度/（m•s−1）转向/
rad纵向加
速度/（m•s−2）符号 dr vr vx θ ax
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表 3 多目标收益函数权重的学习结果
Table 3. Learning results of multi-objective payoff function weights
收益函数维度 特征 权重 安全性 $ {f}_{thwf} $ 0.15 $ {f}_{thwr} $ 0.28 效率性 $ {f}_{\text{v}} $ 0.42 舒适性 $ {f}_{\text{jerk}} $ 0.15
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表 4 实验配置表
Table 4. Experimental configuration table
符合 描述 数值 $ {L}_{\text{car}} $ 车辆长度/m 4.976 $ {W}_{\text{car}} $ 车辆宽度/m 1.908 $ {L}_{\text{lane}} $ 道路长度/m 640 $ {W}_{\text{lane}} $ 道路宽度/m 3.75 $ {v}_{\min } $ 最小行驶速度/（m•s−1） 15 $ {v}_{\max } $ 最大行驶速度/（m•s−1） 36 $ a $ 加速度/（m•s−2） 0～5 $ d $ 减速度/（m•s−2） 0～5 $ \delta $ 前轮转向角度/（°） −3～3
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表 5 不同交通流密度下2种换道模型的事故发生数
Table 5. Number of accidents of two lane-changing models under different traffic flow densities
交通流密度/（辆•km−1） MOBIL换道模型 博弈换道模型 10 0 0 20 0 0 30 0 0 40 0 0 50 0 0 60 1 0 70 0 0 80 2 0 90 3 1 100 1 0
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