Digital road maps with slope, curve, and other road information provide the opportunity to apply model-based predictive control approach, which can help to save fuel, increase safety and comfort, and reduce wear in vehicle operation. The problem is that the maps obtained from different providers have different qualities and that the prediction model that uses slope, curve radius, and other information can only be tested with the map. The method presented in this paper extracts a quality benchmark from the altitude and slope information of different sources together with a vehicle longitudinal dynamic model with only one driving experiment and before the predictive control application is ready or used. The example is a truck model. The maps used include two commercial providers' maps and two self-made maps. The latter use two different GPS1-based technologies to sample the altitude profile of the road. This paper presents methods to evaluate the altitude and slope information from digital road maps, to find local map errors using a vehicle model, to benchmark different maps with a vehicle model, and to find the most suitable map for a predictive control application.
['Peter Kock', 'Ralf Weller', 'Andrzej Ordys', 'Gordana Collier']
Validation Methods for Digital Road Maps in Predictive Control