Citation

Wang D, Gao L, Lan Z, Li W, Ren J, Zhang J, Zhang P, Zhou P, Wang S, Pan J, Manocha D, Yang R. Front. Neurorobotics 2022; 16: e843026.

Copyright

(Copyright © 2022, Frontiers Research Foundation)

DOI

10.3389/fnbot.2022.843026

PMID

35645759

PMCID

PMC9137412

Abstract

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

Language: en

Keywords

fuel efficiency; heavy-duty truck; high-fidelity simulation; reinforcement learning; self-driving