发表机构
School of Vehicle and Mobility, Tsinghua University; KargoBot.AI Inc.（清华大学车辆与运载学院; 卡戈博特人工智能公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布式电动卡车自动驾驶规划，提出nuTruck基准测试。纳入高精度动力学模型，采用多种规划器为基线，利用真实场景闭环评估，可定量评估侧翻风险等，有望成为评估此类规划器的新标准。
AI 中文摘要
传统基于规则的方法在自动驾驶中的主导地位已逐渐被基于学习的方法取代。虽然基于学习的规划器在乘用车上取得了显著成功，但它们在重型卡车，特别是现代分布式电动驱动卡车（DET）上的性能仍 largely 未被探索。为促进基于学习的规划器在 DET 中的研究和应用，本文提出首个高保真基准测试 nuTruck，用于支持大规模神经网络训练和闭环评估。鉴于 DET 的复杂动力学和高侧翻易感性，首先将高精度非线性卡车动力学模型纳入模拟，实现所有车轮独立驱动和转向，并捕捉加速、减速和转弯引起的动态载荷转移，从而在闭环模拟中定量评估侧翻风险。其次，采用多种基于规则和学习的规划器作为 DET 的基线，并评估其在闭环模拟中的性能。最后，利用 nuPlan 数据集的真实驾驶场景进行广泛的闭环评估，不仅分析传统的无碰撞规划性能，还分析规划轨迹的动态安全性。所提出 nuTruck 基准测试有望成为公平、现实地评估 DET 自动驾驶规划器的新标准。
英文摘要
The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches.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.
Comments8 pages, 6 figures, 5 tables
