  Recently, there have been many advances in autonomous driving society,
attracting a lot of attention from academia and industry. However, existing
works mainly focus on cars, extra development is still required for
self-driving truck algorithms and models. In this paper, 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, 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 shows our system's
capacity of mitigating sim-to-real gap. Our code is available at
https://github.com/InceptioResearch/IITS
