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We present AutonoVi:, a novel algorithm for autonomous vehicle navigationthat supports dynamic maneuvers and satisfies traffic constraints and norms.Our approach is based on optimization-based maneuver planning that supportsdynamic lane-changes, swerving, and braking in all traffic scenarios and guidesthe vehicle to its goal position. We take into account various trafficconstraints, including collision avoidance with other vehicles, pedestrians,and cyclists using control velocity obstacles. We use a data-driven approach tomodel the vehicle dynamics for control and collision avoidance. Furthermore,our trajectory computation algorithm takes into account traffic rules andbehaviors, such as stopping at intersections and stoplights, based on anarc-spline representation. We have evaluated our algorithm in a simulatedenvironment and tested its interactive performance in urban and highway drivingscenarios with tens of vehicles, pedestrians, and cyclists. These scenariosinclude jaywalking pedestrians, sudden stops from high speeds, safely passingcyclists, a vehicle suddenly swerving into the roadway, and high-densitytraffic where the vehicle must change lanes to progress more effectively.

AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints