Testing distributed trajectory planning in the cyber-physical mobility lab

Abstract This article presents the testing of distributed trajectory planning algorithms using our rapid prototyping platform, the Cyber-Physical Mobility Lab (CPM Lab). We propose two algorithms for distributed trajectory planning which plan trajectories at intersections, highway on- and off-ramps, and lane changes for networked and autonomous vehicles. The algorithms avoid collisions between vehicles using a synchronization-based and a prioritized Distributed Model Predictive Control (DMPC) strategy. We test two algorithms in the CPM Lab which is able to handle parallel, sequential, and hybrid computations. The CPM Lab achieves reproducible experiments under non-deterministic computation times and stochastic communication times. Our evaluation shows that different algorithms for distributed trajectory planning can be efficiently tested in different in-the-loop tests.