Expanding a bit for those who don't know how a Kalman filters (or any Bayesian recursive estimator for that matter) work here's the essential idea.
Each sensor is given an uncertainty model, that is for example a stochastic model by adding e.g. Gaussian noise to the "true" value it is measuring. Further, you have another model that describes the dynamics, e.g. equations from physics can tell you where the car will go if you know the current speed, position, etc..
1. The kalman filter computes a probabilistic prediction of what it thinks is happening by using the dynamics model. That is, based on what it knows so far, where will the car (probably) be when the next measurement comes up?
2. When measurements from various sensors come in, the Kalman filter uses Bayes's theorem to compute a mean (posterior), in which each measurement is weighted by the probability that the measured value is correct (using the uncertainty models; "correct" here means "in agreement with the prediction"). In other words, sensor that are inaccurate (large variance) will be considered less in the computation of the mean, while more accurate sensors are given more importance.
Once the mean of the measured quantities are computed they are used again in step 1 and the whole thing is repeated. As you can see, that disagreements are justified by the inaccuracies of the sensors, and the process of performing a probabilistic weighted average solves the problem. For the Kalman filter in particular, it can be be show (mathematically proved) that this process minimizes the variance (uncertainty) of the measured quantities (which btw. is an amazing result if you think about it).