Learning of vehicular performance models for longitudinal motion planning to satisfy arrival requirements

Motion planning with predictable timing and velocity will enable a number of interesting applications such as autonomous intersection management (AIM). These planning algorithms depend on an accurate model of the performance of the vehicular controllers, which can be highly non-linear. Au et al. proposed a motion planning algorithm to satisfy the arrival requirements in AIM. However, they assumed that the performance models are given for every road and did not discuss how to learn these models. In this paper, we propose an instance-based learning approach to learn the performance models automatically, and argue that instance-based learning is suitable for this learning task because performance models for different roads can have a high correlation with each other. Moreover, an exploration strategy based on the principle of least effort is given to speed up the learning process. Our experiments showed that the instance-based learning method with distance-based exploration strategy offers a faster learning rate than the artificial neural network methods.