Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Motion Coordination Using Convex Feasible Set Based Model
  Predictive Control   The implementation of optimization-based motion coordination approaches in
real world multi-agent systems remains challenging due to their high
computational complexity and potential deadlocks. This paper presents a
distributed model predictive control (MPC) approach based on convex feasible
set (CFS) algorithm for multi-vehicle motion coordination in autonomous
driving. By using CFS to convexify the collision avoidance constraints,
collision-free trajectories can be computed in real time. We analyze the
potential deadlocks and show that a deadlock can be resolved by changing
vehicles' desired speeds. The MPC structure ensures that our algorithm is
robust to low-level tracking errors. The proposed distributed method has been
tested in multiple challenging multi-vehicle environments, including
unstructured road, intersection, crossing, platoon formation, merging, and
overtaking scenarios. The numerical results and comparison with other
approaches (including a centralized MPC and reciprocal velocity obstacles) show
that the proposed method is computationally efficient and robust, and avoids
deadlocks.

21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int