兼顾舒适与环保，智能优化驾驶风格提升乘车体验
An eco-driving approach for ride comfort improvement
- 基于自组织映射分析驾驶风格，识别影响舒适度的成因
- 可使舒适度指标提升57.7%，碳排放降低47.1%
- 适合自动驾驶系统设计与个性化驾驶建议场景
交通系统正面临新挑战：自动驾驶普及带来乘坐舒适性问题，同时环境污染对气候和健康的影响日益突出。在自动化汽车模型中，驾驶员将转为乘客，更易出现晕动症或不适感。因此，需兼顾环保与舒适性。本文提出一种基于自组织映射（SOM）的方法，从生态驾驶角度评估个体驾驶风格对乘坐舒适性的影响。利用已采集的仪器化车辆数据集，对驾驶员进行分类，识别其不舒适与非环保的根源。随后生成基于自然语言的个性化建议，以提高系统参与度。实验表明，该方法可使舒适度评价参数提升最高达57.7%，温室气体排放降低最多47.1%。
原文摘要 · Abstract (English)
New challenges on transport systems are emerging due to the advances that the current paradigm is experiencing. The breakthrough of the autonomous car brings concerns about ride comfort, while the pollution concerns have arisen in recent years. In the model of automated automobiles, drivers are expected to become passengers, so, they will be more prone to suffer from ride discomfort or motion sickness. Conversely, the eco-driving implications should not be set aside because of the influence of pollution on climate and people's health. For that reason, a joint assessment of the aforementioned points would have a positive impact. Thus, this work presents a self-organised map-based solution to assess ride comfort features of individuals considering their driving style from the viewpoint of eco-driving. For this purpose, a previously acquired dataset from an instrumented car was used to classify drivers regarding the causes of their lack of ride comfort and eco-friendliness. Once drivers are classified regarding their driving style, natural-language-based recommendations are proposed to increase the engagement with the system. Hence, potential improvements of up to the 57.7% for ride comfort evaluation parameters, as well as up to the 47.1% in greenhouse-gasses emissions are expected to be reached.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。