Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles

Klinsmann Agyei; Pouria Sarhadi; Wasif Naeem

REVIEW 4 major objections 6 minor 1 cited by

Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that a large language model, prompted with COLREGs rules and live collision-risk measurements, can serve as the real-time decision-maker for an autonomous surface vehicle, issuing correct give-way and stand-on commands.

desk verdict A genuine first proof-of-concept of LLM-based COLREGs decision-making for ASVs, but Eq. (2)'s contradictory bearing mapping and anecdotal evaluation undercut the compliance claims. read the letter →

arxiv 2411.16587 v3 pith:XWW4RVEI submitted 2024-11-25 cs.RO cs.SYeess.SY

Klinsmann Agyei , Pouria Sarhadi , Wasif Naeem This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords largelanguagemodelsCOLREGsautonomoussurfacevehiclescollisionavoidanceexplainableAIriskassessmentfuzzylogicwaypointtracking

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Maritime collision regulations are written in natural language for human crews, which makes them hard to encode as fixed rules. The paper argues that this ambiguity is precisely where a large language model belongs: a prompted LLM can read structured encounter data, decide whether the own ship must give way or stand on, and explain its reasoning in plain text. The decision layer runs at 1 Hz while a conventional line-of-sight planner and PD controller execute the chosen manoeuvre on a nonlinear ship model. Simulated crossing, head-on, overtaking, and stand-on scenarios are offered as evidence that the loop remains COLREGs-compliant, tracks waypoints, and keeps the human in the loop through generated explanations. If this holds, COLREGs compliance stops being a hardcoded lookup and becomes a language understanding task.

What carries the argument

The central object is the LLM-based decision function $D(\psi_{\mathrm{rel}}, S) = \mathrm{LLM}(\mathcal{M}, B(\psi_{\mathrm{rel}}), \mathrm{Risk}, S)$, where $\mathcal{M}$ is a maritime prompt template, $B(\psi_{\mathrm{rel}})$ maps relative bearing to one of three encounter classes through a piecewise interval rule, and $S$ is a state vector of situation, action, turning state, and manoeuvre index. This function converts raw encounter geometry and risk into a human-readable rule citation and a steering direction. On the execution side, the mechanism is the composite heading command $\psi_d = \psi_{\mathrm{LOS}} + \psi_{\mathrm{CTE}} + \psi_{\mathrm{COLAV}}$, where the $\psi_{\mathrm{COLAV}}$ term contains the factor $K_{\mathrm{Dir}}$ that carries the LLM's chosen give-way or stand-on decision down to the controller. The risk index that feeds the LLM is computed by a Z-shaped fuzzy membership function over DCPA, TCPA, and range, with thresholds $T_{\mathrm{Risk}}$, $T_R$, $T_{\mathrm{DCPA}}$, and $T_{\mathrm{TCPA}}$.

What would settle it

Run repeated head-on encounters with the target dead ahead ($\psi_{\mathrm{rel}}=0^\circ$), which satisfies both the head-on and overtaking intervals of Eq. (2); if the LLM does not consistently return 'head-on, turn starboard', the bearing classification is not self-sufficient.

Watch

Extended reading notes

Core claim

The central claim is that the encounter classification and manoeuvre-selection parts of COLREGs, which resist crisp algorithmic formulation, can be delegated to a large language model without any maritime fine-tuning. At each decision step the model receives the relative bearing $\psi_{\mathrm{rel}}$, range $R$, DCPA, TCPA, and a normalised fuzzy risk index $\mathrm{Risk}(t)\in[0,1]$, together with a prompt template that encodes Rules 13–17 and a state vector carrying the current situation, action, and turning status. It returns a situation label, a command ('give-way, turn starboard' or 'stand on'), and a reasoning chain. That command sets the direction of a collision-avoidance term in the low-level heading planner, which is tracked by a PD controller on a nonlinear ship model with input saturation and disturbances. The paper reports four encounter scenarios — crossing give-way, head-on, overtaking, and crossing stand-on — in which the decisions match the expected COLREGs behaviour and the vessel completes its waypoint mission.

Load-bearing premise

The load-bearing premise is that encounter classification can be recovered from the relative bearing alone, with the LLM resolving the interval overlap in Eq. (2) correctly.

Editorial extensions

If this is right

COLREGs compliance becomes an online property of the loop: the LLM reclassifies the encounter at 1 Hz and can shift between give-way and stand-on as the risk numbers evolve, rather than following a precomputed manoeuvre table.
Every steering decision carries a text explanation that names the governing rule, so the same output that drives the ship could be logged for audit or displayed to a human supervisor.
The low-level planner and controller do not need to understand COLREGs; they execute the direction set by the decision layer, so the same ship model can be reused with different rule sets.
The reported scenarios (crossing give-way, head-on, overtaking, and crossing stand-on) show the architecture covering the four main encounter types without maritime-specific retraining of the LLM.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: Because the decision layer is prompt-driven rather than retrained, the same interface could be pointed at inland-waterway rules, port traffic schemes, or company standing orders by swapping the prompt template and threshold vector; this extension is implied by the design but not tested in the paper.
Editorial inference: The bearing partition in Eq. (2) makes a dead-ahead target fall in both the head-on and overtaking intervals and places the overtaking sector ahead of the vessel, so the formulas alone do not fix the encounter class; the LLM's interpretation of the prompt is carrying that unresolved ambiguity, and that is worth testing directly.
Editorial inference: The authors' stated limitations (weather, vessel manoeuvrability, traffic density) suggest a direct stress test: vary those factors inside the fuzzy risk thresholds or as prompt context and check whether the LLM's explanations and actions degrade gracefully or flip discontinuously.
Editorial inference: The 1 Hz decision rate and written reasoning output make the system an attractive platform for human-in-the-loop review, but they also mean that any deployment must bound LLM latency and output variability before the loop can close safely.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The quick take: this is a real first demonstration—GPT-4 as a high-level COLREGs decision maker in a closed loop with a nonlinear ship model, risk assessment, and low-level planning/control. The code and prompt templates on GitHub are a genuine asset. But the bearing classification function in Eq. (2) is internally inconsistent, and the evaluation is a handful of single-run scenarios, so the "COLREGs compliance" claim is overreach.

What is new and good: prior work (COLREGs3) tested LLMs on static scenes without dynamics; this paper actually closes the loop with a first-order Nomoto model, input saturation, disturbances, and a PD controller, and the LLM outputs readable reasoning at 1 Hz. The integration of fuzzy risk indices (DCPA/TCPA/Range) into the prompt is a sensible architecture, and the paper is honest about missing weather and traffic density.

The soft spot that matters: Eq. (2). The head-on interval (-6° to 6°) is a proper subset of the overtaking interval (-112° to 112°), so any near-zero bearing is both head-on and overtaking. Overtaking is also centered ahead of the vessel, whereas COLREGs Rule 13 defines it as approach from more than 22.5° abaft the beam. The authors say this mapping is "encoded in the LLM's prompt architecture," so the LLM is being asked to reason with a contradictory sector map. There's no experiment showing whether the LLM follows the flawed mapping or falls back on pretrained maritime knowledge. That makes the internal consistency of the method unverified, not just an aesthetic issue.

Secondary: the evaluation is thin. Four scenarios, one run each, no quantitative metrics (minimum DCPA, cross-track error, time-to-collision), no baseline comparison to a conventional rule-based layer, no repeated trials. The LLM's own explanations are used as evidence of compliance, which is self-referential. This is addressable with a corrected sector definition and a systematic benchmark, so it's not a fundamental rejection.

For a reader: this is a proof of concept for the maritime autonomy community, useful for anyone exploring LLMs for rule-based decision making in safety-critical settings. I'd bring it to a reading group—the flawed Eq. (2) is a good teaching moment. I would not trust the compliance claims without more data, but the idea is sound enough to deserve serious peer review, with major revisions requested: fix the bearing mapping, add quantitative metrics, and moderate the conclusions.

Referee Report

4 major / 6 minor

Summary. The paper proposes an LLM-based (GPT-4) high-level decision-making layer for autonomous surface vehicles under COLREGs. The system classifies encounters (head-on, overtaking, crossing) from relative bearing, risk metrics, and state, and outputs explainable give-way/stand-on commands; these commands are executed by a low-level LOS/CTE/COLAV planner and PD controller on a nonlinear vessel model. The authors claim it is the first application of LLM-based explainable decision-making to COLREGs-constrained ASV control, and they demonstrate the architecture in four simulated encounter scenarios (crossing give-way, head-on, overtaking, crossing stand-on) with qualitative trajectory plots and LLM reasoning traces. The code is open-sourced on GitHub.

Significance. The paper addresses a timely problem: translating ambiguous, human-oriented COLREGs rules into machine-executable decisions in an explainable way. The architecture combines LLM reasoning with conventional planning and control in a modular fashion, and the decision traces are human-interpretable, which is a genuine strength. The open-source implementation and the explicit consideration of online risk assessment are also positive. However, the central claim of demonstrated COLREGs compliance rests on qualitative anecdotes rather than systematic evaluation; if the internal inconsistencies in the bearing classification and the absence of quantitative metrics are addressed, the approach could be a useful contribution to the emerging area of LLM-based maritime autonomy. As presented, the evidence is not yet sufficient to support the paper's claims.

major comments (4)

[Section III-A, Eq. (2)] The bearing classification mapping B(Ψ_rel) is internally inconsistent: the head-on interval (−6° to 6°) is a proper subset of the overtaking interval (−112° to 112°), so a near-zero relative bearing is simultaneously classified as both head-on and overtaking. Furthermore, the overtaking sector is centered ahead of the vessel, whereas COLREGs Rule 13 defines overtaking as approach from more than 22.5° abaft the beam. Because this mapping is explicitly encoded in the LLM prompt, the decision-maker receives contradictory instructions. The paper neither states how this ambiguity is resolved nor tests cases with bearings near the interval boundaries; it also does not verify which source (Eq. (2) or the LLM's pretrained maritime knowledge) actually drives the decisions. This issue is load-bearing for the 'rigorous classification framework' and 'COLREGs compliance' claims.
[Section IV, Figs. 4-6] The evaluation is entirely qualitative. There are no quantitative metrics (e.g., observed CPA/DCPA/TCPA, cross-track error, heading error, control effort, or number of rule violations), no baseline comparison against a conventional rule-based or optimization-based COLREGs-compliant planner, no repeated trials with random seeds or perturbed initial conditions, and no failure analysis. The abstract claims 'accurate waypoint tracking and feasible control' but no data substantiates these terms. A single illustrative run per scenario cannot support the conclusion that the system 'maintains online COLREGs compliance' in general.
[Section III-B, Eqs. (3) and (15)] The risk thresholds T_Risk, T_R, T_DCPA, T_TCPA are declared as fixed constants without justification or sensitivity analysis, and the low-level tuning gains K_p, K_d, K_COLAV, μ, w_R, w_β are not reported in the paper (they are only referred to reference [18]). Since the paper claims reproducible results and provides code, these parameter values and the rationale for the threshold choices should be stated explicitly, along with a sensitivity study showing how the decisions change with threshold variations.
[Section IV, crossing give-way example (Fig. 5)] The example in Fig. 5 is used to 'verify the system's ability to correctly interpret relative bearings,' but the reported relative heading of −150.09° falls outside the overtaking range in Eq. (2) and is thus classified as 'crossing' by default. The example does not demonstrate the resolution of the Eq. (2) ambiguity. Moreover, the paper does not specify how the LLM is instructed to handle headings that are simultaneously in two intervals, so the robustness of the classification remains unverified.

minor comments (6)

[Abstract and Introduction] The abstract states 'first application' and 'first attempt' twice; given that reference [8] already explored LLMs for marine navigation, the novelty claim should be qualified (e.g., 'first to integrate with low-level control and online risk assessment').
[Section II] There are typographical and grammatical issues: 'Also, The absence' has an incorrect capitalization, and 'mirrors trained mariners expertise' should be 'mirrors trained mariners' expertise'.
[Section III-B.3] In the sentence 'to generate the control actions (uc in Eg. 6)', 'Eg.' should be 'Eq.'.
[Figs. 5 and 6] The relative bearing values are given without units; please add 'deg' or the degree symbol to avoid ambiguity.
[Conclusion] The sentence 'While our fuzzy logic risk assessment model effectively combines navigation parameters into a unified risk metric, but has limitations' contains a double subject; please revise to '...unified risk metric, it has limitations'.
[Section III-A] The paper refers to 'a rigorous classification framework' but Eq. (2) is the only formal specification; consider adding a diagram that clearly partitions the bearing space according to COLREGs, including the abaft-the-beam overtaking sector.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the LLM decisions are empirically generated from prompts and sensors, not definitionally forced by fitted inputs.

full rationale

The paper's central claim is an empirical demonstration that a GPT-4-based decision layer, prompted with COLREGs rules and fed online DCPA/TCPA/risk measurements, produces compliant manoeuvre commands that a lower-level planner-controller executes on a nonlinear ship model. No parameter is fitted, and no reported output is defined as the input. The bearing mapping B(psi_rel) in Eq. (2) is a stated design choice encoded in the prompt, not a quantity derived from the results, so the scenario outcomes are not forced by construction; the LLM could have failed to follow the mapping, and the paper documents concrete actions it actually produced. Citation [18] supplies the vehicle model, LOS/COLAV planning, PD controller, and CPA risk formulas, which are external, falsifiable components rather than the LLM decision claim being tested; the self-citation is therefore not load-bearing. The evaluation is partly self-referential in that the COLREGs rules used as the compliance benchmark are the same rules placed in the prompt, but this tests instruction-following rather than exhibiting a reduction of a prediction to its inputs. The contradictory overlap of the head-on and overtaking sectors in Eq. (2) is a correctness or validity concern about the classification framework, not a circularity. The Conclusion explicitly acknowledges limitations such as weather, vessel manoeuvrability, and traffic density, confirming that the claim is a bounded empirical one rather than a self-justifying derivation. No circular step meeting the evidentiary standard could be identified.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on hand-chosen thresholds, a bearing classification rule with an internal overlap, and domain assumptions about the ship model and risk assessment. Low-level planning gains come from the authors' earlier publication [18]. No new physical entities are introduced.

free parameters (4)

Risk thresholds T_Risk, T_R, T_DCPA, T_TCPA = 0.75, 1000 m, 250 m, 60 s
Hand-chosen in Eq. (3); they define when a situation is considered urgent and therefore influence the information the LLM receives.
Bearing classification boundaries = plus or minus 6 degrees (head-on), plus or minus 112 degrees (overtaking)
Constants in Eq. (2) that define situation classification; the overtaking interval overlaps with head-on, making the mapping ambiguous.
LLM temperature = 0.2
Chosen in Section III-A to stabilize outputs; it changes the exact wording of the LLM's explanations.
Low-level tuning gains K_p, K_d, K_COLAV, mu, w_R, w_beta = not specified in the paper
Required by Eqs. (9)-(12) and taken from the authors' prior work [18]; the ship's ability to execute LLM decisions depends on them.

assumptions (5)

domain assumption The nonlinear ship model in Eq. (6) adequately represents an ASV for validating decision-making.
All simulation results use this model; behavior may differ on real vessels.
domain assumption The fuzzy risk index Eq. (15) computed from DCPA, TCPA, and range captures the risk relevant to COLREGs decisions.
The risk index is the primary numeric input to the LLM and is not calibrated against encounter data.
ad hoc to paper The bearing map Eq. (2) partitions relative headings into mutually exclusive COLREGs situations despite overlapping intervals.
Head-on lies inside overtaking in the interval definitions, so the assumption of a well-defined function is violated and the LLM is expected to paper over it.
domain assumption GPT-4 can reliably map numeric encounter data and COLREGs prompts to correct give-way or stand-on decisions.
This is the central capability the paper asserts; it is tested on only four scenarios.
domain assumption A 1 Hz decision rate is fast enough for safe collision avoidance at 32 knots.
Stated in Section IV without sensitivity analysis or safety margins.

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Cite this review

Pith. "Pith review of Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles." pith.science (2026). https://pith.science/paper/XWW4RVEI

@misc{pith2026241116587,
  author       = {Pith},
  title        = {Pith review of: Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XWW4RVEI}},
  note         = {Machine review of arXiv:2411.16587}
}

read the original abstract

In the field of autonomous surface vehicles (ASVs), devising decision-making and obstacle avoidance solutions that address maritime COLREGs (Collision Regulations), primarily defined for human operators, has long been a pressing challenge. Recent advancements in explainable Artificial Intelligence (AI) and machine learning have shown promise in enabling human-like decision-making. Notably, significant developments have occurred in the application of Large Language Models (LLMs) to the decision-making of complex systems, such as self-driving cars. The textual and somewhat ambiguous nature of COLREGs (from an algorithmic perspective), however, poses challenges that align well with the capabilities of LLMs, suggesting that LLMs may become increasingly suitable for this application soon. This paper presents and demonstrates the first application of LLM-based decision-making and control for ASVs. The proposed method establishes a high-level decision-maker that uses online collision risk indices and key measurements to make decisions for safe manoeuvres. A tailored design and runtime structure is developed to support training and real-time action generation on a realistic ASV model. Local planning and control algorithms are integrated to execute the commands for waypoint following and collision avoidance at a lower level. To the authors' knowledge, this study represents the first attempt to apply explainable AI to the dynamic control problem of maritime systems recognising the COLREGs rules, opening new avenues for research in this challenging area. Results obtained across multiple test scenarios demonstrate the system's ability to maintain online COLREGs compliance, accurate waypoint tracking, and feasible control, while providing human-interpretable reasoning for each decision.

Figures

Figures reproduced from arXiv: 2411.16587 by the authors.

**Figure 2.** The algorithm structure for design- and run-time training of the LLM-based decision maker incorporating risk [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Waypoint following scene and its parameters [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Illustration of the ASV motion in different scenarios. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: illustrates a moment in the crossing scenario, demonstrating the LLM’s decision-making process in a critical situation. The trajectory plot (left) shows a developing crossing scenario with converging vessels. The algorithm processes a set of urgent parameters: high r…

**Figure 6.** Figure 6: decision-making, crossing stand-on with proper COLREGs compliance. This scenario particularly highlights our system’s sophisticated state awareness capabilities. Even with elevated risk values, the LLM maintains appropriate stand-on behaviour while monitoring the sit…

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

18 extracted references · 14 canonical work pages · cited by 1 Pith paper

[18]

An integrated risk assessment and collision avoidance methodology for an autonomous catamaran with fuzzy weighting functions,

P. Sarhadi, W. Naeem, and N. Athanasopoulos, “An integrated risk assessment and collision avoidance methodology for an autonomous catamaran with fuzzy weighting functions,” in 2022 UKACC 13th International Conference on Control (CONTROL) , 2022, pp. 228–234

work page 2022
[1]

A survey on multimodal large language models for autonomous driving,

C. Cui et al. , “A survey on multimodal large language models for autonomous driving,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 958–979

work page 2024
[2]

An LLM-driven framework for multiple-vehicle dispatching and navigation in smart city landscapes,

R. Chen and et. al., “An LLM-driven framework for multiple-vehicle dispatching and navigation in smart city landscapes,” in 2024 IEEE International Conf. on Rob. and Aut. (ICRA) , 2024, pp. 2147–2153

work page 2024
[3]

Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving,

L. Chen, O. Sinavski, J. H ¨unermann, A. Karnsund, A. J. Willmott, D. Birch, D. Maund, and J. Shotton, “Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 14 093–14 100

work page 2024
[4]

A survey of recent machine learning solutions for ship collision avoidance and mission planning,

P. Sarhadi, W. Naeem, and N. Athanasopoulos, “A survey of recent machine learning solutions for ship collision avoidance and mission planning,” IF AC-PapersOnLine, vol. 55, no. 31, pp. 257–268, 2022

work page 2022
[5]

COLREGs and their application in collision avoidance algorithms: A critical analysis,

J. A. G. Maza and R. P. Arg ¨uelles, “COLREGs and their application in collision avoidance algorithms: A critical analysis,” Ocean Engi- neering, vol. 261, p. 112029, 2022

work page 2022
[6]

Path planning and collision avoidance for autonomous surface vehicles i: a review,

A. Vagale, R. Oucheikh, R. T. Bye, O. L. Osen, and T. I. Fossen, “Path planning and collision avoidance for autonomous surface vehicles i: a review,” Journal of Marine Science and Technology , pp. 1–15, 2021

work page 2021
[7]

Drplanner: Diagnosis and repair of motion planners for automated vehicles using large language models,

Y . Lin, C. Li, M. Ding, M. Tomizuka, W. Zhan, and M. Althoff, “Drplanner: Diagnosis and repair of motion planners for automated vehicles using large language models,” IEEE Robotics and Automation Letters, 2024

work page 2024

Show all 18 references

[8]

COLREG 3 - exploring the potential of large language models in ma- rine navigation systems,

L. Sanchez-Heres, R. Weber, F. Ahlgren, F. Olsson, and O. Lundstr ¨om, “COLREG 3 - exploring the potential of large language models in ma- rine navigation systems,” https://research.chalmers.se/en/publication/ 540274, February 2024

2024
[9]

Multiple ships cooperative navigation and collision avoidance using multi-agent reinforcement learning with communication,

Y . Wang and Y . Zhao, “Multiple ships cooperative navigation and collision avoidance using multi-agent reinforcement learning with communication,” arXiv preprint arXiv:2410.21290 , 2024

2024 arXiv
[10]

LLM-assisted light: Leveraging large language model capabilities for human-mimetic traffic signal control in complex urban environments,

M. Wang, A. Pang, Y . Kan, M.-O. Pun, C. S. Chen, and B. Huang, “LLM-assisted light: Leveraging large language model capabilities for human-mimetic traffic signal control in complex urban environments,” arXiv preprint arXiv:2403.08337 , 2024

2024 arXiv
[11]

Driving everywhere with large language model policy adaptation,

B. Li, Y . Wang, J. Mao, B. Ivanovic, S. Veer, K. Leung, and M. Pavone, “Driving everywhere with large language model policy adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 14 948–14 957

2024
[12]

Dilu: A knowledge-driven approach to autonomous driving with large language models,

L. Wen, D. Fu, X. Li, X. Cai, T. Ma, P. Cai, M. Dou, B. Shi, L. He, and Y . Qiao, “Dilu: A knowledge-driven approach to autonomous driving with large language models,” arXiv preprint arXiv:2309.16292 , 2023

2023 arXiv
[13]

Languagempc: Large language models as decision makers for autonomous driving,

H. Sha, Y . Mu, Y . Jiang, L. Chen, C. Xu, P. Luo, S. E. Li, M. Tomizuka, W. Zhan, and M. Ding, “Languagempc: Large language models as decision makers for autonomous driving,” arXiv preprint arXiv:2310.03026, 2023

2023 arXiv
[14]

Drive like a human: Rethinking autonomous driving with large language models,

D. Fu, X. Li, L. Wen, M. Dou, P. Cai, B. Shi, and Y . Qiao, “Drive like a human: Rethinking autonomous driving with large language models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 910–919

2024
[15]

London, U.K.: International Maritime Organization, 1972

COLREGs—Convention on the International Regulations for Prevent- ing Collisions at Sea, London . London, U.K.: International Maritime Organization, 1972

1972
[16]

Ship collision avoidance and COLREGs compliance using simulation-based control behavior selection with predictive hazard assessment,

T. A. Johansen, T. Perez, and A. Cristofaro, “Ship collision avoidance and COLREGs compliance using simulation-based control behavior selection with predictive hazard assessment,” IEEE transactions on intelligent transportation systems , vol. 17, no. 12, pp. 3407–3422, 2016

2016
[17]

Explainable arti- ficial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions,

S. Atakishiyev, M. Salameh, H. Yao, and R. Goebel, “Explainable arti- ficial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions,” IEEE Access , 2024

2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [18]

An integrated risk assessment and collision avoidance methodology for an autonomous catamaran with fuzzy weighting functions,

P. Sarhadi, W. Naeem, and N. Athanasopoulos, “An integrated risk assessment and collision avoidance methodology for an autonomous catamaran with fuzzy weighting functions,” in 2022 UKACC 13th International Conference on Control (CONTROL) , 2022, pp. 228–234

work page 2022

[2] [1]

A survey on multimodal large language models for autonomous driving,

C. Cui et al. , “A survey on multimodal large language models for autonomous driving,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 958–979

work page 2024

[3] [2]

An LLM-driven framework for multiple-vehicle dispatching and navigation in smart city landscapes,

R. Chen and et. al., “An LLM-driven framework for multiple-vehicle dispatching and navigation in smart city landscapes,” in 2024 IEEE International Conf. on Rob. and Aut. (ICRA) , 2024, pp. 2147–2153

work page 2024

[4] [3]

Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving,

L. Chen, O. Sinavski, J. H ¨unermann, A. Karnsund, A. J. Willmott, D. Birch, D. Maund, and J. Shotton, “Driving with LLMs: Fusing object-level vector modality for explainable autonomous driving,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 14 093–14 100

work page 2024

[5] [4]

A survey of recent machine learning solutions for ship collision avoidance and mission planning,

P. Sarhadi, W. Naeem, and N. Athanasopoulos, “A survey of recent machine learning solutions for ship collision avoidance and mission planning,” IF AC-PapersOnLine, vol. 55, no. 31, pp. 257–268, 2022

work page 2022

[6] [5]

COLREGs and their application in collision avoidance algorithms: A critical analysis,

J. A. G. Maza and R. P. Arg ¨uelles, “COLREGs and their application in collision avoidance algorithms: A critical analysis,” Ocean Engi- neering, vol. 261, p. 112029, 2022

work page 2022

[7] [6]

Path planning and collision avoidance for autonomous surface vehicles i: a review,

A. Vagale, R. Oucheikh, R. T. Bye, O. L. Osen, and T. I. Fossen, “Path planning and collision avoidance for autonomous surface vehicles i: a review,” Journal of Marine Science and Technology , pp. 1–15, 2021

work page 2021

[8] [7]

Drplanner: Diagnosis and repair of motion planners for automated vehicles using large language models,

Y . Lin, C. Li, M. Ding, M. Tomizuka, W. Zhan, and M. Althoff, “Drplanner: Diagnosis and repair of motion planners for automated vehicles using large language models,” IEEE Robotics and Automation Letters, 2024

work page 2024