The internal model is an important piece of the control system of an autonomous driving vehicle. In order for the model to deliver accurate predictions, a valid model structure and well chosen parameters are needed. Model parameters can be highly fluctuating or complex to predict, especially when looking into tyre ground surface interaction models. Instead of predicting parameter values beforehand, they could be estimated and updated in real-time. Fluctuation or incorrectness can be adjusted while driving. However, this uncertainty in parameter value must be accounted for when applying control. Solving this problem by regarding the uncertainty in parameters of the internal vehicle model as a POMDP has been researched in this paper. The research question being: is it worthwhile to use the POMDP approach for online parameter estimation of autonomous passenger vehicles? To answer this multiple sub-questions have been composed. We start off looking into: what is the most suitable vehicle model? Different vehicle models and tyre models were compared. Literature showed the bicycle model in combination with the linearized tyre model to be most suitable for autonomous passenger vehicles. The next question is: What is the most promising algorithm? Using literature, suitable algorithms for solving this POMDP have been found and compared. From three compelling algorithms, the one best fitting the autonomous driving criteria was chosen. Knowing the model and the algorithm for the simulation the next question became: Does the algorithm perform on a vehicle model? To answer this question, the simulation has been implemented in MATLAB and performance has been tested. The results showed significant increase in parameter estimation performance. Within 2 timesteps the estimate had converged correctly. The next question is: Does the algorithm perform within realistic bounds? To answer this question, the same simulation as before has been used, but now with saturation on the steering input. This showed parameter estimation performance increase compared to the original trajectory, but not as overwhelming as without saturation. The next question is: Does the algorithm suffer from high noise? To answer this question, the same simulation has been used, but now with different levels of noise. The results showed parameter estimation performance significantly affected by increasing noise. The final sub-question is: Does the algorithm suit increasing model complexity? To answer this question, the amount of parameters have been increased in the simulation and there has been looked into the large matrices that accompany the algorithm. Results showed that increasing the complexity has a significant effect on the size of the simulation and algorithm matrices. In conclusion, from all of these experiments arose some very interesting results. This produced a useful insight into the strengths and weaknesses of the POMDP algorithm performing on a passenger vehicle, answering the research question. This also led to various recommendations for future research. Interesting would be altering the belief filter to enhance performance on bounded steering input and increased sensor noise.