Fuel-Optimal Operational Speed Planning for Autonomous Trucking on Highways

The rapid advancement of autonomous driving technology, particularly in autonomous trucking on highways, shows great value for enhancing efficiency and reducing costs in the logistics industry. In this work, we define the full-trip speed planning problem for autonomous trucks under delivery time and fuel consumption constraints, referred to as the Operational Speed Planning (OSP) problem. To support and accelerate research on the OSP problem, we have developed a comprehensive dataset using a fleet of over 400 trucks. The dataset contains rich, diverse information covering more than 22 million kilometers of real-world highway driving data. In addition to this static dataset, we have developed a closed-loop simulator that allows for the interactive evaluation of OSP solutions, enabling researchers to test speed planning strategies in a realistic environment. Furthermore, we provide an OSP baseline method based on dynamic programming to optimize speed planning, balancing the delivery time requirements and fuel consumption. Our extensive experiments demonstrate both the accuracy of the simulation and the effectiveness of the OSP baseline in planning optimal speeds, proving its capability to meet time constraints while improving fuel efficiency. The dataset, simulator, and baseline will be made publicly available to foster further research and innovation in this area.