We propose a distributed trajectory generation method for connected autonomous vehicles.
It is integrated in an intersection crossing scenario where we assume a given vehicle order provided by a high-level scheduling unit.
The multi-vehicle framework is modeled by local independent vehicle dynamics with coupling constraints between neighboring vehicles.
Each vehicle in the framework computes in parallel a local model predictive control (MPC) decision, which is shared with its neighbors after conducting a convex Jacobi update step.
The procedure can be iteratively repeated within a sampling time-step to improve the overall coordination decisions of the multi-vehicle setup.
However, iterations can be stopped after each inter-sampling step with a guaranteed feasible solution which satisfies local and coupling constraints.
We construct feasible initial trajectory candidates and propose a method to emulate the centralized solution.
This makes the Jacobi algorithm suitable for distributed trajectory generation of autonomous vehicles in low and medium speed driving.
%Furthermore, we require that vehicles enter a safe crossing zone satisfying a safety distance constraint.
Simulation results compare the performance of the distributed Jacobi MPC scheme with the centralized solution and illustrate the feasibility guarantee in an intersection scenario with unforeseen events.
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We propose a distributed trajectory generation method for connected autonomous vehicles.
It is integrated in an intersection crossing scenario where we assume a given vehicle order provided by a high-level scheduling unit.
The multi-vehicle framework is modeled by local independent vehicle dynamics with coupling constraints between neighboring vehicles.
Each vehicle in the framework computes in parallel a local model predictive control (MPC) decision, which is shared with its neighbors after...
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