TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

TL;DR

TraInterSim is a novel hybrid-driven traffic intersection simulation method that adaptively combines data-driven optimization with velocity models to generate realistic, planning-aware agent behaviors without extensive parameter tuning.

Contribution

The paper introduces TraInterSim, a hybrid simulation approach that adaptively integrates optimization-based data-driven schemes with velocity models for realistic intersection traffic simulation.

Findings

Generates realistic heterogeneous agent behaviors at intersections.

Operates at interactive rates suitable for practical applications.

Validated through extensive experiments and user studies.

Abstract

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.