Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories

TL;DR

The paper presents Transfusor, a transformer-based generative model that creates realistic, controllable human-like lane-changing trajectories to improve virtual simulation testing for autonomous driving systems.

Contribution

It introduces a novel transformer diffusor model for generating high-fidelity, controllable lane-changing trajectories, enhancing the realism of simulation scenarios for autonomous vehicle testing.

Findings

Effectively learns spatiotemporal lane-changing behaviors

Generates trajectories closely mimicking real human driving

Enhances the realism of virtual simulation scenarios

Abstract

With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems. The virtual simulation test (VST) has become a prominent approach for testing autonomous driving systems (ADS) and advanced driver assistance systems (ADAS) due to its advantages of fast execution, low cost, and high repeatability. However, the success of these simulation-based experiments heavily relies on the realism of the testing scenarios. It is needed to create more flexible and high-fidelity testing scenarios in VST in order to increase the safety and reliabilityof ADS and ADAS.To address this challenge, this paper introduces the "Transfusor" model, which leverages the transformer and diffusor models (two cutting-edge deep learning generative technologies). The primary objective of the Transfusor model is to generate highly realistic and controllable human-like lane-changing trajectories in highway scenarios. Extensive experiments were carried out, and the results demonstrate that the proposed model effectively learns the spatiotemporal characteristics of humans' lane-changing behaviors and successfully generates trajectories that closely mimic real-world human driving. As such, the proposed model can play a critical role of creating more flexible and high-fidelity testing scenarios in the VST, ultimately leading to safer and more reliable ADS and ADAS.