A Study of Vehicle Driving Condition Recognition Using Supervised Learning Methods

In vehicle operation, in order to maximize the fuel economy, the propulsion system control can easily adapt to pressure and temperature variations as these variations can be measured by sensors. However, it is challenging to detect driving cycles. With growing progress made in the artificial intelligence field, pattern recognition gains momentum in various applications. This study presents a study on driving cycle pattern recognition based on supervised learning. Training data 2-D visualization is achieved by t-SNE algorithm. Ten out of twelve supervised learning algorithms predict driving conditions with the accuracy at 88% or higher and Extra Tree algorithm leads the recognition accuracy at 90.26%. To improve recognition accuracy, two hierarchical frameworks are proposed by integrating multiple supervised learning methods using weighted average and vote methods. The two hierarchical frameworks boost the driving condition recognition accuracy from 90.26% to 90.43% (weighted average) and 91.76% (vote). The results are further validated in holdout test. Additionally, a plug-in hybrid electric vehicle simulation shows 3.88%-5.82% fuel economy improvement when compared to baseline method. Two hierarchical methods outperform the Extra Tree method by 2% fuel economy. In summary, supervised learning shows great potential to detect driving cycles for vehicle energy saving.