Towards comfort-optimal trajectory planning and control

ABSTRACT

This paper describes a method to analyse and evaluate different trajectory planning methods and controller types for usage in automated vehicles. Its application is shown by using a novel trajectory planning approach considering comfort aspects (based on Rapidly Exploring Random Tree (RRT)), two different controllers to follow the planned path (cascade controller and flatness based controller) and a simulation method to obtain resulting lateral vehicle accelerations. The method is used to plan and drive a trajectory through a roundabout. It can be seen that the lateral accelerations of the controller-driven vehicle are in the range of the values used for planning. However, the results of both controllers show differences in lateral deviation and in smoothness of lateral accelerations. The simulation results are then compared to real-world test drives in the same roundabout. The measured lateral accelerations are in the same range as well but show a smoother progression than the two controller models.     

Measuring dissimilarity of geographically dispersed space�Ctime paths

In activity-travel analysis, sequences are analysed both in space and time. From this perspective, sequence alignment methods (SAM) are used to value the dissimilarity of sequences. However, only a limited number of research efforts account for spatial characteristics of activity-travel sequences. Additionally, the existing techniques considering spatial characteristics are mainly suited to compare sequences within a small study area. Therefore, the present research re-designs a multidimensional dissimilarity measure which enables identifying dissimilarities between sequences which are geographically dispersed. This technique includes transforming the geographical coordinates of activity locations to Angle/Arc Length (AAL)-trajectories to capture the relative geographical movements within each sequence. These AAL-trajectories form the basis of the subsequent multidimensional sequence alignment analysis aimed at estimating the dissimilarity between activity-travel sequences. This approach proves to compare activity-travel sequences based on the relative positions of the activity locations within sequences, rather than founded on the distances between the absolute geographical locations, as is the case in the traditional sequence alignment methods.