AutoRVO: Local Navigation with Dynamic Constraints in Dense Heterogeneous Traffic
This addresses navigation challenges for autonomous vehicles and robots in complex, mixed-traffic environments, representing an incremental improvement over existing methods.
The paper tackles the problem of computing collision-free navigation for heterogeneous road agents in dense traffic by developing an optimization-based algorithm that accounts for dynamic constraints, achieving improved performance over prior reciprocal collision avoidance schemes in real-world scenarios.
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.