InterSim: Interactive Traffic Simulation via Explicit Relation Modeling

Abstract-- Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existing approaches learn an agent model from large-scale driving data to simulate realistic traffic scenarios, yet it remains an open question to produce consistent and diverse multiagent interactive behaviors in crowded scenes. To overcome this Compared to real-world road testing, simulation offers a challenge, [6] adds a task loss to penalize collisions and [7] more time and resource efficient alternative by reconstructing proposes a feasibility check on the generated trajectories rare but important traffic scenarios. Instead of requiring a allows simulating risky scenarios that are usually difficult hand-crafted loss or an ad-hoc filter, [8] offers simulation to obtain in real-world driving. It fails to produce reactive behavior of models rely on probabilistic sampling and suffer from environment agents when the ego plan diverges from the producing rare or dangerous scenarios, which are crucial to original log and thus becomes less useful in interactive testing autonomous driving planners.