fleet operation managers

Abstract: Most fuel oil consumption (FOC) models for voyage planning are per-ship models and thus generalize poorly beyond each vessel's records. Transfer and federated learning have been reported to perform better, but under different protocols that often use inputs unavailable before departure or random data splits. It therefore remains unclear which approach performs best. In this study, we compare 11 per-ship, adaptation-based, and global methods on 21 vessels. To prevent leakage, all methods receive identical inputs known or forecast before departure, excluding engine RPM and shaft power, and are tested on intact voyage legs after all training records of each vessel. Global pooling, in which one model describes each vessel by its ship identity and seven ship specifications, performs best overall in every setting examined. For in-fleet vessels, Global LightGBM reaches an overall MAPE of 8.92%, against 11.14% for Per-Ship LightGBM, and outperforms every per-ship and adaptation-based model. Global models of five architectures, including a Transformer, all outperform the per-ship and federated models. Fine-tuning a global model for each vessel, even by low-rank adaptation of the Transformer, adds no accuracy. Without any records of a vessel, the global model reaches a median MAPE of 11.20% and outperforms a per-ship model trained on that vessel's records for 14 of 21 vessels. Adding its first records to the pooled data is as accurate as vessel-specific adaptation. Ship identity and specifications are largely interchangeable for in-fleet vessels, but only the specifications support zero-shot prediction. Its error grows as a vessel's specifications differ from the fleet's. Per-ship models and vessel-specific adaptation are therefore unnecessary under fair comparison. One global model can serve a whole fleet, including newly delivered vessels with similar specifications.

Abstract: Crash-rate comparisons between automated driving systems (ADS) and human drivers require an exposure denominator that only a few operators publish. We instead ask a composition question: given a crash, does the mix of crash types differ between ADS and human-driven vehicles? The question needs no mileage and can therefore cover every ADS operator. We assemble the NHTSA Standing General Order (SGO) ADS incident files across both file generations into 2,450 distinct in-scope incidents (2021-2026), of which 828 fall within the 2021-2024 window covered by the Crash Report Sampling System (CRSS), the national police-reported human comparator. A schema-constrained large language model (LLM) codes each SGO narrative into the CRSS crash-type taxonomy. Because SGO compels reporting far below the police-reporting threshold, we bracket reportability with an objective severity floor and a reasonable-person rubric, and claim a difference only where it survives both. Two composition differences hold under every reportability definition, tow definition and weighting of the human side, and under both definitions of driverless operation: turn-across-path crashes are under-represented among ADS crashes (0.23-0.46x the CRSS share under the primary driverless definition) and so are single-vehicle crashes (0.19-0.56x). In severity-floor rear-end crashes the ADS vehicle is the striking party at a quasi-induced-exposure ratio of 0.121 (95% CI [0.05, 0.23]) against a human baseline of 1.000. If the ADS vehicle is also struck more often than its exposure implies, this ratio overstates the ADS-human difference. We also document three pitfalls in the federal data: an operator-specific coding of driverless operation, a tow-away criterion confounded by fleet recovery practice, and an occupancy confound that accounts for about 42% of the apparent ADS-human severity gap.

Abstract: Offline reinforcement learning enables reward-driven policy improvement from fixed datasets without requiring online exploration, making it particularly attractive in safety-critical domains. A central challenge, however, is distribution shift: policy optimization may favor actions that are weakly supported by the offline data, rendering value estimates unreliable. Existing approaches primarily control this shift in the policy's own action space. In interactive environments such as autonomous driving, this can be insufficient: a candidate ego trajectory may remain well supported under the marginal behavior distribution while being poorly supported jointly with the surrounding-agent behavior observed in the logged interaction. We refer to this degradation in interaction support as \emph{interaction distribution shift} (IDS), and introduce \emph{Interaction-Constrained Drive Policy} (ICDP), an offline reinforcement learning framework that explicitly controls interaction-level distribution shift. Starting from the joint data distribution over ego and surrounding-agent futures, we show that joint-support degradation decomposes exactly into an ego-support component and a residual interaction-support component. We recover the latter through contrastive density-ratio estimation, isolating interaction compatibility without explicit joint-density modeling, surrounding-agent prediction, or rollouts in reactive simulators or learned world models during policy optimization. Closed-loop evaluations on nuPlan, Interplan and real-world truck experiments show that ICDP suppresses high-value yet interaction-unsupported trajectory selections and improves performance in interaction-critical driving scenarios. Project webpage: https://mahmoud-selim.github.io/ICDP/