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Connected and automated vehicles provide a new opportunity for highlyadvanced collision avoidance, in which several cars cooperate to reach anoptimal overall outcome, that no single car acting in isolation could achieve.For example, one car may automatically swerve to allow another to avoid anobstacle. However, this requires solving the challenging problem of decidingwhat joint trajectories an ad-hoc group of cooperating vehicles should follow,with no obvious leader known in advance. To avoid the complexities of agreeingwhat plan to follow in an ever-evolving situation, a protocol requiring noleader and no explicit inter-vehicle agreement is desirable, which neverthelessyields cooperative, robust behaviour. One method is demonstrated here, insimulation. This uses the notion of "desired" versus "planned" trajectories,allowing vehicles to influence each other for mutual benefit, without requiringa leader or explicit agreement protocol. Essentially the desired trajectory isthat which the vehicle would choose if other cooperating vehicles were notpresent, avoiding the predicted paths of non-cooperating actors. The plannedtrajectory additionally accounts for the planned trajectories of othercooperating vehicles, giving the safest currently available path. Bothtrajectories are broadcast. As each vehicle attempts to (weakly) avoid thedesired trajectories of other vehicles, cooperative behaviour emerges. A simpleform of model predictive control is used. The cost function penalises predictedcollisions, accounting for severity. There is a weak preference for maintainingthe current road lane, but no explicit reference trajectory. This decentralisedplanning and simple optimisation scheme results in effective handling of a widerange of collision scenarios, with no hard limit to the number of cooperatingvehicles. The computing cost is linear in the number of vehicles.

Decentralised Cooperative Collision Avoidance with Reference-Free Model Predictive Control and Desired Versus Planned Trajectories