  Advances in vehicular communication technologies are expected to facilitate
cooperative driving. Connected and Automated Vehicles (CAVs) are able to
collaboratively plan and execute driving maneuvers by sharing their perceptual
knowledge and future plans. In this paper, an architecture for autonomous
navigation of tight multi-lane platoons travelling on public roads is
presented. Using the proposed approach, CAVs are able to form single or
multi-lane platoons of various geometrical configurations. They are able to
reshape and adjust their configurations according to changes in the
environment. The proposed architecture consists of two main components: an
offline motion planner system and an online hierarchical control system. The
motion planner uses an optimization-based approach for cooperative formation
and reconfiguration in tight spaces. A constrained optimization scheme is used
to plan smooth, dynamically feasible and collision-free trajectories for all
the vehicles within the platoon. The paper addresses online computation
limitations by employing a family of maneuvers precomputed offline and stored
on a look-up table on the vehicles. The online hierarchical control system is
composed of three levels: a traffic operation system (TOS), a decision-maker,
and a path-follower. The TOS determines the desired platoon reconfiguration.
The decision-maker checks the feasibility of the reconfiguration plan. The
reconfiguration maneuver is executed by a low-level path-following feedback
controller in real-time. The effectiveness of the approach is demonstrated
through simulations of three case studies: 1) formation reconfiguration 2)
obstacle avoidance, and 3) benchmarking against behavior-based planning in
which the desired formation is achieved using a sequence of motion primitives.
Videos and software can be found online
https://github.com/RoyaFiroozi/Centralized-Planning.
