Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers
Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers
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DOI：
10.1109/access.2022.3163267
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Naren Bao;L. Capito;Dong-Hyuk Yang;Alexander Carballo;C. Miyajima;K. Takeda
中科院分区：
文献类型：
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作者：
Naren Bao;L. Capito;Dong-Hyuk Yang;Alexander Carballo;C. Miyajima;K. Takeda
目前在自动驾驶汽车控制领域的大多数研究都假设所有车辆都遵循相同的自动驾驶行为模式，从而导致系统具有“保守”或“平均”驾驶风格。然而，对于喜欢更激进驾驶风格的司机来说，这些系统可能无法接受，而极其谨慎的司机可能会认为标准输出过于激进。为了解决这一问题，本文引入了风险敏感控制（RSC），这是一种逆最优控制算法，它可以估计风险敏感的驾驶特征并将其合并到后退水平控制器中。RSC使用元学习算法更新成本函数的参数，随着从用户和主观风险反馈中收集到越来越多的驾驶数据，不断在线改进控制器。估计器通过调整成本函数及其约束，在主观风险分析中考虑驾驶特征和周围车辆位置方面的个体差异。我们使用了五种变道场景来测试这种方法，其中有些是安全的，有些是危险的，在CARLA模拟环境中有30名真实驾驶员。我们的定量和定性评估表明，所提出的框架能够在变道过程中生成用户首选的驾驶动作，即控制命令用户关联的主观风险较低，在复制用户自己的驾驶行为方面优于传统的基于模型的预测控制方法。
Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with “conservative” or “average” driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user’s preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user’s own driving behavior.