The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties.

中文翻译：

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遗传算法与模糊系统相结合，优化自动驾驶汽车概念开发的用户集中度

个性化和共享化的大趋势将极大地改变我们的消费者行为。产品用户的需求将是其开发的核心输入。当前的开发流程不适合此产品开发；因此，我们提出了一种遗传算法和模糊系统的组合，以用户为中心。我们以自动驾驶汽车概念为例执行新的方法论方法，以证明其实现和功能。遗传算法将所需车辆概念的数量减至最少，以满足用户组的移动性需求，而模糊系统将用户需求转换为与车辆相关的属性，这些属性当前已输入以用于车辆概念开发。要介绍此方法，我们使用一个典型的家庭及其潜在的流动性行为。我们的方法优化了其最少数量的车辆概念，以满足所有出行需求，并推导了车辆概念的属性。通过将我们的方法整合到整个车辆概念开发过程中，可以在个性化和共享大趋势的背景下以用户为中心设计自动驾驶汽车。总而言之，我们的方法使我们能够针对定性描述的，异构的用户需求，得出优化数量的产品，并确定与产品相关的属性。