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.

翻译：自动驾驶的决策制定仍是一项具有挑战性的任务。为使相关算法能够集成至实际平台，其必须保证乘客的安全与舒适性，同时确保可解释性及合理的计算时间。为建模并求解这一决策问题，我们提出了一种名为 COR-MP（基于资源守恒理论的机动规划模型）的新方法。该模型基于资源守恒理论——一种应用于人类行为的心理学概念。COR-MP 综合考虑舒适性、安全性及能耗等多种驾驶参数，实时生成效益值，从而量化决策对决策主体的影响。我们通过基于 RTMaps 中间件的闭环仿真对所提方法进行了测试与验证，并在实车上对 COR-MP 进行了初步测试，获得了初步结果。