Leveraging Human Driving Preferences to Predict Vehicle Speed

Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver’s driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon.

I. Introduction

Vehicle speed prediction has essential theoretical value and widespread applications for intelligent vehicles. The pre-known future velocity can significantly reduce energy consumption and emissions of vehicle propulsion systems [1], [2], better understand the traffic environment for advanced driver assistance systems [3]–[5], and improve the battery lifetime and available mileages of electric vehicles [6]. Motivated by these above, existing studies have developed various speed forecasting approaches using different information. However, accurate on-road vehicle speed prediction is still challenging due to the influence of many factors such as traffic condition, vehicle type, and driver behavior [7].