用资源守恒理论优化自动驾驶变道决策，兼顾安全与舒适。
COR-MP: Conservation of Resources Model for Maneuver Planning
- 基于心理资源理论构建驾驶决策模型，融合舒适、安全等参数
- 实时输出决策收益值，量化动作对驾驶员的影响
- 已在仿真与实车测试中验证，适合自动驾驶系统集成
自动驾驶的决策制定仍具挑战性。为实现真实平台部署，算法需保障乘客安全与舒适，同时具备可解释性和合理计算耗时。为此，我们提出一种名为COR-MP（资源守恒模型用于变道规划）的新方法。该模型基于资源守恒理论——一种解释人类行为的心理学概念。COR-MP综合考虑舒适性、安全性、能耗等多种驾驶参数，实时生成一个收益值，用于量化决策对决策主体的影响。该方法通过RTMaps中间件进行闭环仿真测试，并在真实车辆上完成了初步验证，结果表明其具备实际应用潜力。
原文摘要 · Abstract (English)
Decision-making for automated driving remains a challenging task. For their integration into real platforms, these algorithms must guarantee passenger safety and comfort while ensuring interpretability and an appropriate computational time. To model and solve this decision-making problem, we have developed a novel approach called COR-MP (Conservation of Resources model for Maneuver Planning). This model is based on the Conservation of Resources theory, a psychological concept applied to human behavior. COR-MP is based on various driving parameters, such as comfort, safety, or energy, and provides in real-time a profit value that enables us to quantify the impact of a decision on the decision-maker. Our method has been tested and validated through closed-loop simulations using RTMaps middleware, and preliminary results have been obtained by testing COR-MP on a real vehicle.
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