With the increasing number and complexity of Advanced Driver Assistance Systems (ADAS) and rising control facility by individual controllable drives in electric vehicles (EV) the reliability of sensor signals becomes more and more important in nowadays vehicles. In order to enhance the safety, the estimation of vehicle states and parameters gets more relevant. In most state of the art functions a vehicle observer secures the correctness of the delivered states. As the performance of observers depends on their input signals a novel plausibility check is implemented. In this paper the checked signals serve the designed adaptive vehicle observer, based on Extended Kalman filtering technique, as input signals. Thus the integrated vehicle functions can control the electric actuators with more precision in order to improve the driving performance and a minimization of energy consumption by an optimal use of the available road traction. The presented plausibility check improves the overall performance of the vehicle observer towards its robustness and confidence by dealing with faulty or missing sensor signals. So the safety demands that arose by new VDC systems, especially in electric driven vehicles, could be fulfilled with regard to ISO 26262. Here detection and correction mechanisms could avoid a violation of technical safety caused by wrong sensor signals, without any special sensor systems. Next steps for the development of the introduced plausibility check are the offset compensation for the steering wheel, the design of a new concept for the replacement of missing sensor signals based on the Markov chain theory, the algorithm optimisation concerning its real-time ability and the final tuning when the prototype hits the road.