  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.
