实时的监控和驾驶员反馈，促进燃油效率驾驶
Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving
摘要：
提高车辆的燃油效率对于降低成本和保护环境至关重要。虽然高效的发动机和车辆设计以及智能路线规划是提高燃油效率的众所周知的解决方案，但研究还表明，采用燃油效率驾驶行为可以进一步节省成本。在这项工作中，我们提出了一个新的框架，以促进燃料效率的驾驶行为，通过实时的【形】自动监测和驾驶员的反馈。在这个框架中，一个基于随机森林的分类模型，使用历史数据来识别燃油效率低的驾驶行为。分类器考虑驾驶员相关参数，如速度和加速/减速模式，以及环境参数，如交通，道路地形和天气，以评估一分钟驾驶事件的燃油效率。当检测到低效率的驾驶行为时，使用模糊逻辑推理系统来确定驾驶员应该做什么来保持燃料效率的驾驶行为。然后，通过智能手机以非侵入式方式将决定的动作传达给驾驶员。使用一个数据集，从长途巴士，我们证明了所提出的分类模型产生的准确率为85.2%，同时提高燃油效率高达16.4%。
英文摘要：
Improving the fuel efficiency of vehicles is imperative to reduce costs and protect the environment. While the efficient engine and vehicle designs, as well as intelligent route planning, are well-known solutions to enhance the fuel efficiency, research has also demonstrated that the adoption of fuel-efficient driving behaviors could lead to further savings. In this work, we propose a novel framework to promote fuel-efficient driving behaviors through real-time automatic monitoring and driver feedback. In this framework, a random-forest based classification model developed using historical data to identifies fuel-inefficient driving behaviors. The classifier considers driver-dependent parameters such as speed and acceleration/deceleration pattern, as well as environmental parameters such as traffic, road topography, and weather to evaluate the fuel efficiency of one-minute driving events. When an inefficient driving action is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior. The decided action is then conveyed to the driver via a smartphone in a non-intrusive manner. Using a dataset from a long-distance bus, we demonstrate that the proposed classification model yields an accuracy of 85.2% while increasing the fuel efficiency up to 16.4%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
