Paper Citation Record · LEDGER

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.14903.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.14903 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:49:31.036962Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3492e9c0-9358-4737-80c8-bfc1db285ab5 · outbound

This paper cites Perception, planning, control, and coordination for autonomous vehicles,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Perception, planning, control, and coordination for autonomous vehicles,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:37.751350Z

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Observation 3b6abe3d-6902-4636-a055-cdec7ebd8ea1 · outbound

This paper cites A comprehensive review on safe reinforcement learning for au- tonomous vehicle control in dynamic environments,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning A comprehensive review on safe reinforcement learning for au- tonomous vehicle control in dynamic environments,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:37.502073Z

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Observation d85358ad-701f-4f30-b19e-523d954954f0 · outbound

This paper cites Review of decision-making and planning approaches in automated driving,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Review of decision-making and planning approaches in automated driving,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:37.251095Z

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Observation 2cd886d2-560f-43a9-8246-d32ac2107bf5 · outbound

This paper cites Survey on artificial intelligence for vehicles,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Survey on artificial intelligence for vehicles,

Reference 4

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Observation fba102ca-8cc9-4a2f-bece-de92c1ea9b7e · outbound

This paper cites Decision-making technology for autonomous vehicles: Learning-based methods, applications and future outlook,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Decision-making technology for autonomous vehicles: Learning-based methods, applications and future outlook,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:36.567907Z

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Observation 089b7c61-03e5-4f43-b01c-fd8326e4d59c · outbound

This paper cites Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:36.200213Z

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Observation f766d0a4-f8ea-47c8-87ee-d93694b1b07b · outbound

This paper cites Robust lane change decision for autonomous vehicles in mixed traffic: A safety-aware multi-agent adversarial reinforcement learning approach,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Robust lane change decision for autonomous vehicles in mixed traffic: A safety-aware multi-agent adversarial reinforcement learning approach,

Reference 7

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Observation 1de7d1b2-ab7d-4a71-9451-2e7605a1ccac · outbound

This paper cites An environment for autonomous driving decision- making,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning An environment for autonomous driving decision- making,

Reference 8

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Observation ab25397c-ccbe-4fe3-9722-97ac9fec2bc0 · outbound

This paper cites DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

Reference 9

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Source-reported events for the cited work

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Observation 13eab54e-2747-4b28-85bb-921c96aa060e · outbound

This paper cites Investigating high-level decision making for automated driving,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Investigating high-level decision making for automated driving,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:35.774360Z

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Observation 7aef1229-18a1-443a-afe8-f759cfa6c35f · outbound

This paper cites Designing an interpretability analysis framework for deep reinforcement learning (drl) agents in highway automated driving simulation,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Designing an interpretability analysis framework for deep reinforcement learning (drl) agents in highway automated driving simulation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:35.527686Z

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Observation 505e94fb-cfb8-45db-b43f-82935817959e · outbound

This paper cites A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles

Reference 12

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Source-reported events for the cited work

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Observation 25cf916a-82f3-4946-a386-74920d393fc7 · outbound

This paper cites Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:49:31.986642Z

Source-reported events for the cited work

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Observation 9ea7711e-d514-4491-a7b6-b1db65c52edf · outbound

This paper cites Hierarchical reinforcement learning: A comprehensive survey,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Hierarchical reinforcement learning: A comprehensive survey,

Reference 14

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Observation a15c42ea-db17-4300-a326-5f48945fb23f · outbound

This paper cites Trajectory planning for autonomous vehicles using hierarchical reinforcement learning,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Trajectory planning for autonomous vehicles using hierarchical reinforcement learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:35.381425Z

Source-reported events for the cited work

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Observation a0fc35ea-d09d-4689-b575-fc41fb1a884e · outbound

This paper cites Action and trajectory planning for urban autonomous driving with hierarchical reinforcement learning,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Action and trajectory planning for urban autonomous driving with hierarchical reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:35.149274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f30a9ce-4de3-4a41-a898-2653c34a8e20 · outbound

This paper cites Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

Reference 17

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Observation 8a5dd971-0d72-4ed7-8572-94ae7a01b463 · outbound

This paper cites Combining decision making and trajec- tory planning for lane changing using deep reinforcement learning,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Combining decision making and trajec- tory planning for lane changing using deep reinforcement learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:35.019035Z

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Observation 39948c68-66c5-4876-bb1d-733e2e132932 · outbound

This paper cites Reinforcement learning,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Reinforcement learning,

Reference 19

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Observation de5f30b7-b1f9-404b-8ddb-deff20c0106d · outbound

This paper cites Reinforcement learning: An introduction,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Reinforcement learning: An introduction,

Reference 20

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Observation 63cde26c-4e26-4e09-af15-aba3fd0566ef · outbound

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CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Unresolved cited work

Reference 21

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Observation 7c2047de-6660-42c2-95ca-92fddd5b2c0f · outbound

This paper cites An efficient centralized multi-agent reinforcement learner for cooperative tasks,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning An efficient centralized multi-agent reinforcement learner for cooperative tasks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:34.154968Z

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Observation 84092647-64b7-436b-9877-aa5646af6a4e · outbound

This paper cites Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning

Reference 23

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Observation f166d797-1e8a-445b-8c10-35bf3ba105f3 · outbound

This paper cites Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling,

Reference 24

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Observation 66f45b37-7a2b-42b0-a694-a2ac1abc4345 · outbound

This paper cites Long short-term memory,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Long short-term memory,

Reference 25

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Observation 6cc9f661-dc4a-47c1-8493-027fcd831669 · outbound

This paper cites Attention is all you need,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Attention is all you need,

Reference 26

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Observation 39b08bb4-af4c-4609-89fd-c34ac325a96c · outbound

This paper cites Educational applications of the cyber-physical mobility lab: A summary,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Educational applications of the cyber-physical mobility lab: A summary,

Reference 27

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Observation 4d597e6f-1e8e-41ce-ba2a-c8d45487c0b9 · outbound

This paper cites Sigmarl: A sample-efficient and gen- eralizable multi-agent reinforcement learning framework for motion planning,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Sigmarl: A sample-efficient and gen- eralizable multi-agent reinforcement learning framework for motion planning,

Reference 28

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Observation ec40af7a-6b14-4ef2-93f9-b2a7574937bd · outbound

This paper cites Attention Is All You Need.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Attention Is All You Need

Reference 29

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Observation 00dfa195-4bfe-4463-8406-e7dcd78981ba · outbound

This paper cites Learning-based control barrier function with provably safe guarantees: Reducing conservatism with heading-aware safety margin,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Learning-based control barrier function with provably safe guarantees: Reducing conservatism with heading-aware safety margin,

Reference 30

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Observation eea7015c-8afb-42d1-8fb3-5047b987c073 · outbound

This paper cites A real-time control barrier function- based safety filter for motion planning with arbitrary road boundary constraints,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning A real-time control barrier function- based safety filter for motion planning with arbitrary road boundary constraints,

Reference 31

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Observation 5b1664db-6ed7-4982-9043-3011620b7e0a · outbound

This paper cites Lanelets: Efficient map repre- sentation for autonomous driving,.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Lanelets: Efficient map repre- sentation for autonomous driving,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:33.204479Z

Source-reported events for the cited work

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Observation 3f6b82be-ee79-495c-a5a7-28340e90c581 · outbound

This paper cites High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation

Reference 35

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Observation e314a9f8-bf40-473b-8dfb-9afd5b5f57e2 · outbound

This paper cites Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

Reference 2020

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Observation 8d8d47ab-ce7d-4e2f-af24-39a4d6fa910a · outbound

This paper cites Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning.

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning

Reference 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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