AutoRVO: Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic

Abstract

We present a novel algorithm for computing collision-free navigation for heterogeneous road-agents such as cars, tricycles, bicycles, and pedestrians in dense traffic. Our approach currently assumes the positions, shapes, and velocities of all vehicles and pedestrians are known and computes smooth trajectories for each agent by taking into account the dynamic constraints. We describe an efficient optimization-based algorithm for each road-agent based on reciprocal velocity obstacles that takes into account kinematic and dynamic constraints. Our algorithm uses tight fitting shape representations based on medial axis to compute collision-free trajectories in dense traffic situations. We evaluate the performance of our algorithm in real-world dense traffic scenarios and highlight the benefits over prior reciprocal collision avoidance schemes.

References in corpus (2)

• CARLA: An Open Urban Driving Simulator
• AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

Cited by in corpus (5)

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• AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points
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