工业运动学轨迹模型（IKTM）：无坐标自回归生成器
Industrial Kinematic Trajectory Model (IKTM): Coordinate-Free Autoregressive Generator
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中文总结 AI 辅助
提出无坐标自回归轨迹生成器IKTM，用运动学序列表示工业车辆运动，零样本迁移到新站点，匹配转弯分布且终止准确。
中文摘要 AI 辅助
移动性模拟支持工业环境（如港口、矿山和机场）中的物流、安全和通信规划。然而，现有的轨迹模型依赖于绝对坐标、路网令牌或语义区域：这些表示是特定于站点的，不适合非结构化的工业地形。我们引入了工业运动学轨迹模型（IKTM），一种无坐标轨迹生成器，通过运动学序列（速度和航向变化）表示工业车辆运动，无需绝对空间参考。IKTM使用具有概率混合头的自回归因果变换器，并通过深度序列建模和显式持续时间条件信号扩展了我们先前无坐标马尔可夫模型。在一个站点上训练并在三个未见站点上进行零样本评估，它匹配了保留遥测数据的经验转弯率分布的小转弯形状；在所有四个站点上，在预言机长度持续时间目标协议下，100个采样种子的每站点平均詹森-香农散度约为0.035-0.050比特，在完全零样本先验长度协议下约为0.032-0.042比特，无论分布外（OOD）持续时间目标是从每个保留站点的经验长度分布还是从站点A先验中抽取，范围相似。两种协议均高于度量的采样噪声底（<=0.0049比特）。按站点配对，预言机长度值比我们先前马尔可夫模型在相同1 Hz协议下的重新实现低5.3-6.0倍。终止是持续时间条件而非空间条件：在100%的试验中，滚动停止，长度跟踪误差为+0.0 +/- 0.0秒，相对于采样目标（100/100完全准确；N=100，T=0.2，未触及的分布内测试分割）。
英文摘要
Mobility simulation supports logistics, safety, and communications planning in industrial environments such as ports, mines, and airports. Existing trajectory models, however, rely on absolute coordinates, road-network tokens, or semantic zones: representations that are site-specific and not well suited to unstructured industrial terrain. We introduce the Industrial Kinematic Trajectory Model (IKTM), a coordinate-free trajectory generator that represents industrial vehicle motion through kinematic sequences (speed and heading change) with no absolute spatial reference. IKTM uses an autoregressive causal transformer with probabilistic mixture heads and extends our prior coordinate-free Markovian model with deep sequence modelling and an explicit duration-conditioning signal. Trained on one site and evaluated zero-shot on three unseen sites, it matches the small-turn shape of the empirical turn-rate distributions of held-out telematics; across all four sites, the per-site mean Jensen-Shannon divergence over 100 sampling seeds spans approximately 0.035-0.050 bits under an oracle-length duration-target protocol and approximately 0.032-0.042 bits under a fully zero-shot prior-length protocol, with similar ranges whether out-of-distribution (OOD) duration targets are drawn from each held-out site's empirical length distribution or from the Site A prior. Both protocols stay above the metric's sampling-noise floor (<=0.0049 bits). Paired by site, the oracle-length values are 5.3-6.0x lower than those of a re-implementation of our prior Markovian model under the same 1 Hz protocol. Termination is duration-conditioned rather than spatial: rollouts stop on 100% of trials with a length-tracking error of +0.0 +/- 0.0 s against the sampled target (100 of 100 exactly on target; N=100, T=0.2, untouched in-distribution test split).
发表机构
- University of Otago（奥塔哥大学）
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