Action-integrated modeling and optimization of energy-related driver-vehicle interaction

能源效率日益成为人机系统的设计标准。对于车辆控制等动态环境，非常需要对人在环环境中的能效建模、优化和表示进行跨学科研究。为此，该项目整合了两条研究中心线：（1）驾驶员和情境适应性能源相关优化策略研究（面向控制的能源效率建模和优化）和（2）能源相关驾驶员-车辆交互研究（心理建模和能源相关行为调节优化）。在这个项目中，这两条研究线被结合到一个基于工程心理学和控制工程的建模和优化电池电动汽车（BEV）中与能源相关的驾驶员-车辆交互，通过行动集成的能源效率表示。重点在于（1）作为这种优化的一部分的驾驶员的表示和（2）能量效率的不同表示方法对（a）人机系统的综合性能和（B）用户在动作调节期间的体验的影响的实证调查，特别注意多样性特征（用户多样性）的作用。该项目考虑了一种控制概念，其中驾驶员直接参与能量最优控制，并在控制策略中明确考虑（驾驶员在环）。人类行为调节（例如，在确保安全的同时，对时间效率、能源效率和舒适度等目标进行情景权衡）不会被自动车辆控制所取代，而是由能量动态的行为集成表示（通知系统）辅助。因此，该项目研究了与能源相关的人机耦合的控制工程方法，迄今为止几乎没有任何研究工作。[项目阶段-1]追求3个研究目标：（1）开发和校准综合驱动能源模拟，（2）开发理论方法基础，以及（3）分析，综合和测试能源相关的驾驶场景。在此基础上，[项目阶段-2]通过（1）面向感觉运动的，（2）面向效率的，（3）基于规则的最佳行为表示，通过能量显示研究人机耦合的3种结构方法。该项目的关键假设是，与根据能量/距离度量的能量效率的传统表示相比，这些基于优化的能量效率表示可以在能量效率和动作调节的用户体验方面实现优势。

Energy efficiency is increasingly becoming a design criterion for human-machine systems. For dynamic contexts such as vehicle control, there is a great need for interdisciplinary research on the modeling, optimization, and representation of energy efficiency in human-in-the-loop settings. To this end, the project integrates two central lines of research: (1) research on driver- and situation-adaptive energy-related optimization strategies (control-oriented modeling & optimization of energy efficiency) and (2) research on energy-related driver-vehicle interaction (psychological modeling & optimization of energy-related action regulation). In this project, both lines of research are combined into an engineering psychology and control engineering based modeling and optimization of energy-related driver-vehicle interactions in battery electric vehicles (BEV) via action-integrated energy efficiency representations. The focus lies on (1) the representation of drivers as part of this optimization and (2) the empirical investigation of the effect of different representation approaches for energy efficiency on (a) the integrated performance of the human-machine system and (b) the experience of users during action regulation, with special attention paid to the role of diversity characteristics (user diversity). The project considers a control concept in which drivers are directly involved in the energy-optimal control and explicitly considered in the control strategy (driver-in-the-loop). Human action regulation (e.g. situational weighing of goals such as time efficiency, energy efficiency and comfort, while ensuring safety) is not replaced by automated vehicle control, but assisted by action-integrated representations of energy dynamics (informing system). The project thus investigates a control engineering approach to energy-related human-machine couplings, on which hardly any research work has been done so far. [Project Phase-1] pursues 3 research goals: (1) the development and calibration of an integrated driving-energy simulation, (2) the development of theoretical-methodological foundations, and (3) the analysis, synthesis, and testing of energy-relevant driving scenarios. [Project Phase-2] investigates on this basis 3 structural approaches to human-machine coupling via energy displays through (1) sensorimotor-oriented, (2) efficiency-oriented, and (3) rule-based representations of optimal behavior. The key hypothesis of the project is that these optimization-based representations of energy efficiency can achieve advantages in terms of energy efficiency and user experience in action regulation when compared to conventional representations of energy efficiency according to energy/distance metrics.