AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
This addresses the problem of safe and efficient autonomous driving in complex traffic environments for autonomous vehicle developers, though it appears incremental in combining existing techniques.
The authors tackled autonomous vehicle navigation by developing an optimization-based planning algorithm that supports dynamic maneuvers while satisfying traffic constraints, achieving successful performance in simulated urban and highway scenarios with tens of vehicles, pedestrians, and cyclists.
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.