Terrain-Adaptive Cruise Control: A Human-Like Approach

Abstract

With rapid advancements in the field of autonomous vehicles, intelligent control systems and automated highway systems, the need for GPS based vehicle data has grown in importance. This has provided for a plethora of opportunities to improve upon the existing vehicular systems.

In this study, the use of GPS data for optimal regulation of vehicle speed is explored. A discrete dynamic programming algorithm with a model predictive control (MPC) scheme is employed. The objective function is formulated in such a way that the weighting gains vary adaptively based on the road slope. Unlike in the prevalent approaches, this eliminates the need for a preprocessing algorithm to ensure tracking along flat stretches of road.

Fuel savings of 0.48% along a downhill have been recorded. Also, the usage of brakes has been considerably reduced due to deceleration prior to descent. This is highly advantageous, particularly in the case of heavy-duty vehicles as they are prone to wearing of brake pad lining. Therefore, this method proves to be a simpler alternative to the existing methods, while incorporating the best attributes of a human driver and the tracking ability of a conventional controller.