Optimized vehicle speed planning based on road profile estimation

This research work aims to examine the effects of road pavement roughness on critical vehicle operating characteristics such as ride comfort and fuel consumption. The proposed approach is based upon onboard sensors like Inertial Measurement Units (IMUs) or smartphone sensors to measure vertical accelerations and pitching angles of the vehicle for accurate estimation of the road profile state. To ensure that the road profile data is being constantly updated and improved, a cloud-based crowdsourcing platform is integrated to provide real-time and dynamically updated road condition information. This paper proposes a predictive speed-planning algorithm as a solution to the problem of integrating this road profile information for the purpose of simultaneously achieving good ride comfort and fuel efficiency. The algorithm formulates objective functions that reduce passenger discomfort by minimizing vertical vibrations and pitching of the vehicle and minimizes fuel consumption by optimizing the driving speed profiles. The key constraints that are incorporated into the algorithm are maximum and minimum allowable speeds, safety margins, vehicle dynamic performance, traffic rules, and road geometry. The developed methodology has the potential to be used as a control algorithm for autonomous vehicle speed control and as an Advanced Driver Assistance System (ADAS) that advises human drivers about the most efficient speeds to maintain. Safe, comfortable, and energy-efficient vehicle operations are the outcomes of this research that enables proactive adaptation of vehicle speeds according to the anticipated road roughness.