CRUISE: Multi-Domain Frameworks and Applications

• CRUISE is a multifaceted term defined differently across domains, encompassing constant-velocity motion in robotics, adaptive control in vehicles, and specialized software frameworks for literature review and network analysis.
• Applications of CRUISE range from real-time trajectory planning in autonomous systems and safety-critical automotive interventions to V2X scene reconstruction and maritime research campaigns.
• The literature highlights trade-offs in performance metrics such as travel time, computational load, and energy efficiency, emphasizing the importance of constrained regulation in diverse technical settings.

CRUISE is used in the cited literature in several non-equivalent senses. It denotes a motion regime in path-constrained trajectory planning; a family of cruise-control architectures for automobiles, vehicular platoons, high-speed trains, quadrotors, and boats; and several acronymic systems, including a living-literature-review toolbox, a dynamic model-selection system for network traffic analysis, and a cooperative V2X reconstruction-and-synthesis framework based on Gaussian Splatting (Shen et al., 2018, Kusa et al., 2023, Xu et al., 24 Jul 2025). A consistent theme is constrained regulation—of velocity, spacing, safety margins, computation, or scene structure—but the technical object named “CRUISE” depends strongly on domain.

1. Scope and principal research usages

In the supplied corpus, the term spans control theory, robotics, transportation, scientific software, computer vision, networking, and maritime field science. In control papers, “cruise” may denote either constant path velocity or longitudinal regulation relative to a leader vehicle. In systems papers, CRUISE is often an acronym naming an integrated software framework. In maritime studies, “cruise” may refer to a research-vessel campaign or to cruise-vessel traffic as an environmental source (Aghav et al., 2011, Zabrocki et al., 2021, Müller et al., 12 Sep 2025).

Usage	Technical meaning	Representative source
Cruise motion	Constant path velocity with $\ddot s = 0$ on a path	(Shen et al., 2018)
Cruise control mode	Automatic throttle/brake intervention at high threat	(Aghav et al., 2011)
Connected/adaptive cruise control	Longitudinal control with spacing and safety constraints	(Bohara et al., 2023)
CRUISE-Screening	Living literature review toolbox	(Kusa et al., 2023)
Cruise Control	Dynamic model selection for network traffic analysis	(Hugon et al., 2024)
CRUISE	V2X reconstruction and editing using Gaussian Splatting	(Xu et al., 24 Jul 2025)
Cruise report / cruise vessels	Research expedition or cruise-ship traffic context	(Müller et al., 12 Sep 2025)

A recurrent misconception is to treat all occurrences as variants of automotive cruise control. The cited literature shows a broader taxonomy: some usages are descriptive motion primitives, some are safety-critical controllers, and some are acronymic software or reconstruction frameworks.

2. Cruise as a motion regime in robotics and autonomous platforms

In path-constrained trajectory planning, “cruise motion” is defined as constant path velocity along an interval of the path, so that $\dot s=\text{const}$ and $\ddot s=0$. On the $(s,\dot s)$ plane, these are horizontal segments that satisfy the path-acceleration constraints. The framework in "Real-time Acceleration-continuous Path-constrained Trajectory Planning With Built-in Tradability Between Cruise and Time-optimal Motions" introduces a user-specified constant upper bound $\dot s \le \varepsilon$, defines $M(s)=\varepsilon$, and reconstructs the admissible ceiling as $MVC^*(s)=\min(MVC(s),M(s))$ (Shen et al., 2018). A constant-velocity boundary $L(s)$ distinguishes where $\ddot s=0$ is dynamically feasible; feasible cruise segments $\underline{M}$ are then treated as switch arcs. The resulting pipeline combines PreCompute, CNI, and BIO, where BIO smooths acceleration discontinuities while preserving completeness. The paper proves three monotonic relations: with increasing $\dot s=\text{const}$0, travel time decreases and tends to the time-optimal value, cruise proportion decreases, and computational time increases. Its simulation table makes the trade-off explicit: at $\dot s=\text{const}$1, travel time is 11.36 s, computation time is 1 ms, and cruise proportion is 94%; at $\dot s=\text{const}$2, travel time is 6.10 s, computation time is 45 ms, and cruise proportion is 0% (Shen et al., 2018).

The same term is reinterpreted in aerial robotics. "A Model Predictive Control Approach for Quadrotor Cruise Control" defines cruise control as hovering point stabilization, reference tracking around hover, and robust hover under a constant disturbance that simulates a gust of wind in the $\dot s=\text{const}$3 direction (Chen et al., 17 Apr 2025). The controller is a full-state-feedback MPC for the ideal case and an output-feedback offset-free MPC for disturbance rejection. The design uses a 12-state linearization around hover, zero-order-hold discretization with $\dot s=\text{const}$4 s, a prediction horizon $\dot s=\text{const}$5, and a terminal set with 480 linear inequalities. Reported solve times are about 0.05–0.06 s per step for full-state MPC with $\dot s=\text{const}$6 and about 0.07 s per step for output-feedback offset-free MPC with online optimal target selection (Chen et al., 17 Apr 2025).

A further extension appears in autonomous marine navigation. "Nonlinear Model Predictive Control with Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment" develops a single-layer NMPC for a 12-person tourist cruise boat operating in the Pohang Canal (Lee et al., 2023). The vessel has length overall 7.9 m, beam 2.6 m, draft 0.3 m, and a single outboard gasoline engine. The canal test stretch is along a 1 km canal with average width about 15 m. Canal walls are parameterized as line segments from onboard LiDAR point clouds and imposed as obstacle-avoidance constraints within the NMPC, which simultaneously performs online trajectory planning and tracking (Lee et al., 2023).

3. Cruise control as longitudinal safety regulation in road, platoon, and rail systems

In automotive safety systems, one early formulation treats cruise control primarily as emergency intervention. "Design and Validation of Safety Cruise Control System for Automobiles" proposes a two-layer architecture consisting of a Safety System and a Cruise Control System (Aghav et al., 2011). The Safety System continuously monitors distance, relative speed, and azimuth using a forward-looking automotive radar sensor, compares them against predefined thresholds, warns the driver at low threat, and at the highest threat level activates Cruise Control Mode, which controls engine throttle and, if permitted, the brake system. The ESTEREL implementation includes environment adaptation: in rain, distance = 10 and speed = 18; in mist, distance = 8 and speed = 17; in normal conditions, distance = 5 and speed = 20. The design is compiled to finite-state machines and verified with temporal-logic-style properties in Xeve (Aghav et al., 2011).

Modern adaptive cruise control formulations make safety explicit through invariant sets and barrier conditions. "Adaptive Estimation-Based Safety-Critical Cruise Control of Vehicular Platoons" defines the safety function $\dot s=\text{const}$7 and the safe set $\dot s=\text{const}$8 (Bohara et al., 2023). A continuous-time observer estimates the preceding vehicle’s velocity and acceleration in the absence of V2V communication, while a closed-form control law derived from a control barrier function guarantees safety despite estimation error. The paper reports a string stability gain $\dot s=\text{const}$9 in a sinusoidal leader-velocity experiment, and shows that adding intermittent V2V communication reduces the average estimation error bound from 0.3363 m/s to 0.2932 m/s and the average conservative headway from 0.0364 m to 0.0317 m (Bohara et al., 2023). In "Provably Safe Cruise Control of Vehicular Platoons", the same safety problem is treated with set invariance: infinite-time collision avoidance is guaranteed under bounded additive disturbances while keeping platoon length and leader speed within prescribed ranges, and both centralized and distributed policies are synthesized (Sadraddini et al., 2017).

Energy efficiency becomes a primary objective in connected cruise control. "Energy-efficient Reactive and Predictive Connected Cruise Control" distinguishes reactive ACC/RCCC from predictive PACC/PCCC in mixed traffic with beyond-line-of-sight information from a connected leader several vehicles ahead (Shen et al., 2022). RCCC augments the optimal-velocity-model feedback law with speed terms from connected upstream vehicles; PCCC embeds that information in an MPC through a chain of IDM predictions for hidden vehicles. On real traffic data, the reported energy reductions are 18.1% for RCCC versus RACC and 12.0% for PCCC versus PACC in a step scenario, and 29.2% for RCCC versus RACC and 30.0% for PCCC versus PACC in a congested scenario. In the congested case, PCCC consumes 11.9% less energy than RCCC (Shen et al., 2022). A related data-driven formulation, "Safe and Efficient Data-driven Connected Cruise Control", adds a CBF safety filter to a connected controller that uses motion information from multiple vehicles ahead and reports that optimally utilizing V2V connectivity reduces energy consumption by more than 10% compared to standard non-connected adaptive cruise control (Xiao et al., 29 Jul 2025).

The same longitudinal-control vocabulary extends to fault tolerance and cybersecurity. "Extending Adaptive Cruise Control with Machine Learning Intrusion Detection Systems" analyzes a PID-based ACC augmented by a Kalman filter, shows that injected speed values above a derived threshold can drive the filter off track, and proposes ACC-IDS, in which a binary intrusion flag switches the controller to emergency braking (Othmane et al., 1 Mar 2026). The paper proves that under stated detection-performance and latency constraints, this emergency-braking mode can preserve collision-avoidance guarantees. In railway control, "Asymptotical Cooperative Cruise Fault Tolerant Control for Multiple High-speed Trains with State Constraints" addresses multiple trains with multiple carriages, actuator faults, and distributed state-fault observers (Zhang et al., 2022). The controller enforces asymptotical cooperative cruise while guaranteeing that both position difference and velocity difference of adjacent trains remain in specified ranges throughout the whole process (Zhang et al., 2022).

4. CRUISE as research software and adaptive ML infrastructure

One acronymic use appears in scholarly review automation. "CRUISE-Screening: Living Literature Reviews Toolbox" presents a web-based application for conducting living literature reviews that is designed both for researchers and for developers of automated citation-screening methods (Kusa et al., 2023). The system is built with Python 3.9 and Django 4 on the backend, Bulma and AlpineJS on the frontend, PostgreSQL for reviews and metadata, Elasticsearch for internal indexing, and GROBID for PDF parsing. It queries four data sources—Semantic Scholar API, CORE API, PubMed via Entrez API, and internal document storage—and by default limits each query to the top 500 results per source. Screening supports a strict mode, in which every eligibility question must be answered, and a relaxed mode, in which only the main include/maybe/exclude decision is required. The integrated ML layer includes logistic regression with tf–idf features and fastText for binary classification, requiring at least 3 included and 3 excluded papers before training, and zero-shot prompt-based QA via HuggingFace Text2TextGeneration models such as T0 and T0_3B. Full reviews can be exported as JSON (Kusa et al., 2023).

A second acronymic use is deployment-time adaptation for networking systems. "Cruise Control: Dynamic Model Selection for ML-Based Network Traffic Analysis" targets real-time traffic classification and quality inference under changing load (Hugon et al., 2024). The system pre-trains multiple models with different accuracy-cost tradeoffs, measures feature costs in CPU cycles, extracts a Pareto front, and then selects models online using lightweight signals that reflect current traffic processing ability. Its runtime control loop is AIMD-like: additive increase of model complexity when no drops occur and multiplicative decrease when packet loss is detected through NIC RX queue counters such as rx_miss. Across two traffic-analysis tasks, the paper reports that Cruise Control improves median accuracy by 2.78% while reducing packet loss by a factor of four compared to offline-selected models (Hugon et al., 2024). In this usage, “Cruise Control” names a systems policy for maintaining operational performance under fluctuating computational load rather than a vehicle-motion controller.

5. CRUISE as cooperative V2X reconstruction and synthetic data generation

A third major acronymic use is "CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting" (Xu et al., 24 Jul 2025). This framework reconstructs real-world V2X driving scenes from ego-vehicle and roadside infrastructure sensors, decomposes them into static background and dynamic vehicles, edits traffic participants as Gaussian assets, and renders synchronized ego and infrastructure views for data augmentation. The reconstruction backbone is Street Gaussians, trained jointly with color, depth, normal, sky, semantic, scale, ratio, and regularization losses. Dynamic vehicles are separated using 3D boxes and tracking trajectories, while an ego-mask prevents ego-vehicle annotations from contaminating the static background (Xu et al., 24 Jul 2025).

The editing pipeline combines three components: Street Gaussians for scene reconstruction, TRELLIS for generating 3D Gaussian vehicle assets from Internet images, and GPT-4o for trajectory synthesis conditioned on the vector map and the ego trajectory (Xu et al., 24 Jul 2025). On V2X-Seq, the complete reconstruction stack reports PSNR 27.97, SSIM 0.940, and LPIPS 0.095, while removing the ego-mask degrades these values to PSNR 24.44, SSIM 0.910, and LPIPS 0.125 (Xu et al., 24 Jul 2025). For downstream learning, mixing real and generated data improves multiple tasks: for MonoLSS in ego view, AP$\ddot s=0$0 Easy rises from 56.84 to 61.35; for BevHeight in infrastructure view, AP$\ddot s=0$1 Easy rises from 60.03 to 64.10; and for cooperative ImVoxelNet, AP$\ddot s=0$2 rises from 14.79 to 15.91 while MOTA rises from 21.83 to 25.52 (Xu et al., 24 Jul 2025). In this literature, CRUISE is therefore not a controller but a V2X data-ecosystem component spanning reconstruction, editing, rendering, and benchmark augmentation.

6. Maritime and environmental meanings of cruise

In polar field science, “cruise” retains its operational meaning of a shipborne campaign. "SvalMIZ-25 Svalbard Marginal Ice Zone Campaign 2025 -- Cruise Report" describes a research cruise with KV Svalbard from 22 April to 11 May 2025, aimed at observing the winter Marginal Ice Zone north of Svalbard in order to improve coupled Arctic forecasting systems (Müller et al., 12 Sep 2025). The campaign deployed 21 OpenMetBuoys in the Marginal Ice Zone and measured variables including air temperature, snow and ice temperatures, sea-ice drift, and wave energy spectra. The report emphasizes a distributed observation network designed for representative comparison with gridded model data and situates the cruise within broader efforts on atmosphere–ocean–sea-ice–wave coupling (Müller et al., 12 Sep 2025).

Environmental policy research uses “cruise” differently again, referring to cruise vessels as an emissions source. "Estimating the Local Air Pollution Impacts of Maritime Traffic: A Principled Approach for Observational Data" studies Marseille using 96,432 hourly observations and 4,018 daily observations over 2008–2018, matched to hypothetical randomized experiments via constrained pair matching for time series (Zabrocki et al., 2021). The hourly analysis attributes to cruise-vessel arrivals a +4.7 $\ddot s=0$3g/m$\ddot s=0$4 effect on NO$\ddot s=0$5 at Longchamp in the arrival hour, with a 95% Fisherian interval of [1.4, 8.0] $\ddot s=0$6g/m$\ddot s=0$7, a +4.6 $\ddot s=0$8g/m$\ddot s=0$9 effect on PM$(s,\dot s)$0 at Saint-Louis with interval [0.9, 8.3], a +1.2 $(s,\dot s)$1g/m$(s,\dot s)$2 effect on SO$(s,\dot s)$3 at Longchamp with interval [-0.1, 2.5], and a -3.8 $(s,\dot s)$4g/m$(s,\dot s)$5 effect on O$(s,\dot s)$6 with interval -7.6, 0.0. At the daily level, the paper finds point estimates essentially equal to zero for all pollutants and argues that road traffic has a much larger impact than cruise traffic on daily NO$(s,\dot s)$7 in Marseille (Zabrocki et al., 2021).

Taken together, these maritime usages differ sharply from control-theoretic ones. In one case, a cruise is a data-collection operation; in the other, cruise traffic is a causal treatment whose short-term pollution impacts are estimated from observational time series. Both usages are technical, but neither denotes a controller or an algorithmic acronym.