A Distributed Chance-Constrained MPC Algorithm for Collision Avoidance of CAVs under Stochastic Network Delays in V2V Communication

Sammanfattning

Nowadays, research and application of Connected and Automated Vehicles (CAVs) in Intelligent Transportation Systems (ITSs) have made significant progress, especially in optimization-based motion planning strategies using Model Predictive Control (MPC) algorithms. With the development of nonlinear optimization solvers and high-performance microprocessors, MPC controllers enable CAVs to plan optimal trajectories in real time, navigate to destinations, and avoid collisions with other road users. However, due to the complexity of ITSs, CAVs face the challenge of coupled coordination with different road users (such as other CAVs, pedestrians, and cyclists) that is always accompanied by various uncertainties, which bring additional requirements to MPC algorithms. The deterministic framework of MPC algorithms lacks the ability to incorporate uncertainties into problem formulation, which may lead to CAVs’ unforeseen misalignment with reference trajectories. Consequently, CAVs have more chances of colliding with other road users, deviating from reference trajectories, and getting further away from destinations due to uncertainties, and the safety of ITSs is significantly degraded. To address the aforementioned challenge, this project chooses an Operational Design Domain (ODD), where CAVs plan to cross a traffic intersection, as the case study. In the traffic intersection scenario, CAVs use a Vehicle-to-Vehicle (V2V) communication network to exchange information and coordinate to avoid collisions. The MPC algorithm is formulated in a distributed mannerthatexploitsexchangedinformation, whichmodelsthecoordinationbetween CAVs. Wefocusonstochastic network delays in the V2V communication network and simplify them as Gaussian uncertainties. The effect of Gaussian uncertainties is incorporated into the distributed MPC (DMPC) algorithm. To relax the conservativeness of collision avoidance constraints due to Gaussian uncertainties, we formulate collision avoidance constraints as chance constraints and approximate them based on Chebyshev’s Inequality and the Cumulative Distribution Function (CDF), respectively. The simulation results demonstrate that the distributed chance-constrained MPC (DCCMPC) algorithm reduces the risks of collisions and navigation failures to an acceptable level, which considerably improves the safety of ITSs.