An Intelligent Self-driving Truck System For Highway Transportation

Abstract

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

FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing

“…Therefore, the reliability guarantee in highway interchanges becomes the bottleneck of their wide deployment in highway transport systems [19], [24]. To guarantee safety, researchers have designed different planning and control algorithms to ensure AVs move across interchanges successfully and smoothly [1], [4], [8], [21], [23]. However, these methods are tested under only some specific interchanges and lack a comprehensive evaluation for different interchanges.…”