论文标题
实时监控和驾驶员反馈以促进燃油效率驾驶
Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving
论文作者
论文摘要
提高车辆的燃油效率对于降低成本和保护环境至关重要。尽管有效的发动机和车辆设计以及智能路线计划是提高燃油效率的众所周知的解决方案，但研究还表明，采用燃油效率的驾驶行为可能会带来进一步的节省。在这项工作中，我们提出了一个新颖的框架，以通过实时自动监控和驾驶员反馈来促进燃油效率的驾驶行为。在此框架中，使用历史数据来确定燃油式驾驶行为的基于随机林的分类模型。分类器考虑驾驶员依赖性参数，例如速度和加速/减速模式，以及环境参数，例如交通，道路地形和天气，以评估一分钟驾驶事件的燃油效率。当检测到效率低下的驾驶动作时，使用模糊的逻辑推理系统来确定驾驶员应采取的措施来维持燃油效率高效的驾驶行为。然后，确定的操作通过智能手机以非侵入性的方式传达给驾驶员。使用长距离总线的数据集，我们证明了所提出的分类模型的准确度为85.2％，同时提高燃油效率高达16.4％。
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%.
