Gray-Box Driver Modeling and Prediction: Benefits of Steering Primitives

Shared control is a promising approach for designing an Advanced Driver Assistance System, since it unifies the advantages of both manual control and full automation. However, for a true cooperative shared control ADAS the automation has to understand the human and thus a suitable model which describes the driver in the control loop is essential. Our gray-box approach bases on the biological concept that humans realize motion by combining a finite set of motion primitives (we call movemes). With the assumption that a driver switches between movemes based on perceived information, we propose a Hidden Markov Model which determines the probability of each movement given a certain driving situation. Car turn maneuver experiments show a good approximation of steering trajectories recorded in a driving simulator. A comparison with a black-box model show that the movement-based driver model performs significantly better. In addition, training algorithms are available and the probabilistic approach of the model allows further interpretation of the results.