Incorporating Categorical Information for Enhanced Probabilistic Trajectory Prediction

Advanced Driver Assistance Systems (ADAS) have witnessed a steady increase in complexity during the last few years. Many of these systems could benefit from a reliable long-term prediction of the vehicle's trajectory, for instance the prediction of a turning maneuver at an intersection. The application of probabilistic trajectory prediction provides knowledge of the probability and the uncertainty of the predicted trajectories, allowing a subsequent probabilistic treatment. In this contribution this is achieved by approximating a motion model through a probability density function (pdf) and inferring its parameters with previously observed motion patterns during a training procedure. Predictions can be obtained by calculating statistical parameters of the conditional probability density function (cpdf), for instance the mean and the variance. A common way to obtain the required cpdf is to approximate a joint pdf over the input and output variables and calculate the conditioning. Since the distribution over the input data space is not needed, this can be very wasteful of resources. Therefore in this contribution a novel approach for probabilistic trajectory prediction is proposed which directly approximates the cpdf using Hierarchical Mixture of Experts. Furthermore, the hierarchical structure of the model is exploited to incorporate optional knowledge in terms of categorical information (e.g., turn signal or map information) without the need to directly increase the input parameter space regarding all model components.