Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning

TL;DR

This paper introduces Ctx2TrajGen, a novel framework that generates realistic, context-aware vehicle trajectories for urban driving by leveraging generative adversarial imitation learning, addressing challenges like nonlinear dependencies and data scarcity.

Contribution

It presents a new trajectory generation model that explicitly incorporates surrounding context and road geometry, improving realism and diversity over existing methods.

Findings

Outperforms existing methods in realism and diversity

Effectively handles data scarcity and domain shift

Demonstrates superior performance on the DRIFT dataset

Abstract

Precise modeling of microscopic vehicle trajectories is critical for traffic behavior analysis and autonomous driving systems. We propose Ctx2TrajGen, a context-aware trajectory generation framework that synthesizes realistic urban driving behaviors using GAIL. Leveraging PPO and WGAN-GP, our model addresses nonlinear interdependencies and training instability inherent in microscopic settings. By explicitly conditioning on surrounding vehicles and road geometry, Ctx2TrajGen generates interaction-aware trajectories aligned with real-world context. Experiments on the drone-captured DRIFT dataset demonstrate superior performance over existing methods in terms of realism, behavioral diversity, and contextual fidelity, offering a robust solution to data scarcity and domain shift without simulation.