ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction
Xuan Li, Enlu Liu, Tianyu Shen, Jun Huang, Fei–Yue Wang
Abstract
The latest developments in parallel driving foreshadow the possibility of delivering intelligence across organizations using foundation models. As is well-known, there are limitations in scenario acquisition in the field of intelligent vehicles (IV), such as efficiency, diversity, and complexity, which hinder in-depth research of vehicle intelligence. To address this issue, this manuscript draws inspiration from scenarios engineering, parallel driving and introduces a pioneering framework for scenario generation, leveraging the ChatGPT, denoted as SeGPT. Within this framework, we define a trajectory scenario and design prompts engineering to generate complex and challenging scenarios. Furthermore, SeGPT, in combination with “Three Modes”, foundation models, vehicle operating system, and other advanced infrastructure, holds the potential to achieve higher levels of autonomous driving. Experimental outcomes substantiate SeGPT's adeptness in producing a spectrum of varied scenarios, underscoring its potential to augment the development of trajectory prediction algorithms. These findings mark significant progress in the domain of scenario generation, also pointing towards new directions in the research of vehicle intelligence and scenarios engineering.