Per Sahlholm

A SENSOR AND DATA FUSION ALGORITHM FOR ROAD GRADE ESTIMATION

Abstract 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.

Segmented road grade estimation for fuel efficient heavy duty vehicles

Long haulage road transport consumes considerable amounts of energy in todays 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.

Road Grade Estimation for Look-ahead Vehicle Control

Look-ahead cruise controllers and other advanced driver assistance systems for heavy duty vehicles require high precision digital maps. This contribution presents a road grade estimation algorithm ...

Piecewise Linear Road Grade Estimation

Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade.