This work presented aspects of the actual implementation of a two-dimensional algorithm to obtain road grip quantities. The detection behavior regarding the separate estimates for longitudinal and lateral maximum friction values and its lower bounds was illustrated for pavements of asphalt and snow. It is a promising way to obtain the desired road grip as a result of the statistical regression of fleet operational test data. The ability to conduct such a field test comes from the combined approach of modelbased design and affordable prototype hardware, including automotive bus interfaces. Access to the exclusively used automotive grade sensor information must be ensured and stable in order to reduce costs and time effort. The algorithm itself is encapsulated as a model core in a Simulink library. Thus it is also ready for deployment in offline testing environments. In the future, statistical analysis will show to which extent the desired generalization of road-centered quantities can be achieved. The temporal and spatial road coverage must be judged based an testing different tire and vehicle setups with varying parameters. The test data results from pre-testing for the functional development activities are also available for use in related software development projects. Research into ADAS with autonomous driving intelligence for future innovationResearch into ADAS with autonomous driving intelligence for future innovation.
Road classification from two-dimensional model-based friction estimation
2014
18 Seiten, Bilder, Tabellen, 18 Quellen
Aufsatz (Konferenz)
Englisch
Road classification from two-dimensional model-based friction estimation
Kraftfahrwesen | 2014
|Road classification from two-dimensional model-based friction estimation
Springer Verlag | 2014
|Model-based road friction estimation
Kraftfahrwesen | 2004
|Model-Based Road Friction Estimation
Taylor & Francis Verlag | 2004
|Model-based road friction estimation
Tema Archiv | 2004
|