a b s t r a c tLook-ahead cruise controllers and other advanced driver assistance systems for heavy duty vehicles require high precision digital topographic road maps. This paper presents a road grade estimation algorithm for creation of such maps based on Kalman filter fusion of vehicle sensor data and GPS positioning information. The algorithm uses data from multiple passes over the same road to improve previously stored road grade estimates. Measurement data from three test vehicles and six experiments have been used to evaluate the quality of the obtained road grade estimate compared to a known reference. The obtained final grade estimate compares favorably to one acquired from a specialized road grade measurement vehicle with a DGPS receiver and inertial measurement unit, with an average root mean square error of 0.17% grade.

Abstract-Long haulage road transport consumes considerable amounts of energy in today's world. Predictive control strategies based on digital maps can significantly lower the portion being wasted in traditional cruise control operated highway driving. Such control strategies rely on high quality stored road grade information. This paper describes a newly developed method to estimate the road grade using sensors commonly found on standard heavy duty vehicles. The method utilizes a piecewise linear road model derived from highway design methodologies. The estimation method has been implemented and evaluated experimentally, and is shown to give better results compared to an existing method.

Emerging driver assistance systems, such as look-ahead cruise controllers for heavy duty vehicles, require high precision digital maps. This contribution presents a road grade estimation algorithm for fusion of GPS and vehicle real-time sensor data, with measurements from previous runs over the same road segment. The resulting road grade estimate is thus enhanced using measurements from additional traversals of known roads. Distributed data fusion is utilized to ensure that the storage requirement of known roads does not increase when additional measurements are processed. The implemented algorithm, which is based on extended Kalman filtering and smoothing, is described in detail. Experiments on a Scania test vehicle show the advantages and some of the challenges with the proposed approach.