nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks

2026-07-15

AI Summary

Existing learning-based planning methods for distributed electric-drive heavy trucks (DETs) lack a high-fidelity closed-loop evaluation benchmark. This work proposes nuTruck—the first autonomous planning benchmark specifically designed for DETs—featuring a high-accuracy nonlinear vehicle dynamics model that supports independent control of all wheels for both driving and steering, and enables quantitative assessment of rollover risk. The platform is compatible with both learning-based and rule-based planners, facilitating large-scale closed-loop simulation and data-driven evaluation under realistic driving scenarios. Beyond verifying collision-free performance, nuTruck introduces, for the first time, a systematic quantification of trajectory dynamic safety, thereby establishing a new standard for autonomous planning in DETs.

Abstract

The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based planners have achieved considerable success in passenger vehicles, their performance on heavy-duty trucks, particularly modern distributed electric-drive trucks (DETs), remains largely unexplored. To facilitate research and application of learning-based planners in DETs, this letter presents the first high-fidelity benchmark, called nuTruck, designed to support large-scale neural network training and closed-loop evaluation. Given the complex dynamics and high rollover susceptibility of DETs, we first incorporate a highly accurate nonlinear truck dynamical model into the simulation, which enables independent driving and steering of all wheels and captures dynamic load transfer caused by acceleration, deceleration, and cornering, thereby allowing quantitative assessment of rollover risk in closed-loop simulation. Second, we adapt several rule-based and learning-based planners as baselines for DETs and evaluate their performance in closed-loop simulation. Finally, using real-world driving scenarios from the nuPlan dataset, we conduct extensive closed-loop evaluations, analyzing not only conventional collision-free planning performance, but also the dynamical safety of the planned trajectories. The proposed nuTruck benchmark is expected to serve as a new standard for fair and realistic evaluation of autonomous driving planners on DETs.

Problem

• autonomous driving planning
• distributed electric-drive trucks
• learning-based planners
• rollover risk
• benchmarking

Innovation

• distributed electric-drive trucks
• high-fidelity simulation
• nonlinear truck dynamics
• closed-loop evaluation
• rollover risk assessment