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Predicting future motions of surrounding vehicles and driver's intentions are essential to avoid future potential risks. The predicting future motions, however, is very challenging because the future cannot be deterministically known a priori and there are infinitely many possible future trajectories. Prediction becomes far more challenging when trying to foresee distant future. This paper proposes a probabilistic motion prediction algorithm that can accurately compute the likelihood of multiple target lanes and trajectories of surrounding vehicles by using the artificial neural network; more specifically radial base function network (RBFN). The RBFN prediction algorithm estimates the likelihood of each lane being the driver's target lane in categorical distributions and the corresponding future trajectories in parallel. In order to demonstrate the effectiveness of the proposed prediction algorithm, it is applied for the predictive …