nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks
This benchmark addresses the lack of evaluation tools for autonomous driving planners on heavy-duty trucks, a domain with unique dynamics and safety concerns.
The paper introduces nuTruck, the first high-fidelity benchmark for evaluating learning-based autonomous driving planners on distributed electric-drive trucks (DETs), incorporating a nonlinear truck dynamical model to assess rollover risk. Closed-loop evaluations on real-world scenarios show that existing planners have limited performance on DETs, highlighting the need for specialized approaches.
The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches.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.