An Intelligent Self-Driving Truck System for Highway Transportation
Year: 2022
Type: article
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.
Source: Frontiers in Neurorobotics
Authors: Dawei Wang, Lingping Gao, Ziquan Lan, Wei Li, Jiaping Ren, Jiahui Zhang, Peng Zhang, Pei Zhou, Shengao Wang, Jia Pan, Dinesh Manocha, Ruigang Yang
Institutions: University of Hong Kong, University of Maryland, College Park
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,950
OpenAlex ID: W4226374401
