Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance | Paper Citation Record

Observation 5c3c0bb5-df86-400f-890c-cfa406e35d02 · outbound

This paper cites Road traffic injuries fact sheet,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Road traffic injuries fact sheet,

Reference 1

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Observation 26d76a3d-7d91-412f-8c81-382a0868b8c0 · outbound

This paper cites Traffic safety facts 2023: A compilation of motor vehicle traffic crash data (annual report),.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Traffic safety facts 2023: A compilation of motor vehicle traffic crash data (annual report),

Reference 2

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Observation 7ba83132-168c-41e2-8549-3e6639176e6c · outbound

This paper cites Distracted driving: Cdc transportation safety,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Distracted driving: Cdc transportation safety,

Reference 3

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This paper cites Why anticipatory sensing matters in commercial acc systems under cut-in scenarios: A perspective from stochastic safety analysis,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Why anticipatory sensing matters in commercial acc systems under cut-in scenarios: A perspective from stochastic safety analysis,

Reference 4

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Observation ad7a5458-0fb9-4f77-ad1a-b51914afaf86 · outbound

This paper cites Vehicle-to-Everything Cooperative Perception for Autonomous Driving.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Vehicle-to-Everything Cooperative Perception for Autonomous Driving

Reference 5

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This paper cites Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen

Reference 6

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This paper cites Advancing vulnerable road users safety: Interdisciplinary review on v2x communication and trajectory prediction,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Advancing vulnerable road users safety: Interdisciplinary review on v2x communication and trajectory prediction,

Reference 7

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Observation 47ee20c5-f7f5-4f2e-bcfc-be7b2dc753df · outbound

This paper cites Transformers in vision: A survey,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Transformers in vision: A survey,

Reference 8

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This paper cites Do vision transformers see like con- volutional neural networks?,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Do vision transformers see like con- volutional neural networks?,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.657930Z

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Observation b9f393c1-3e50-44b9-8f1c-883478c5e55d · outbound

This paper cites Cmoa: Contrastive mixture of adapters for generalized few-shot continual learning,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Cmoa: Contrastive mixture of adapters for generalized few-shot continual learning,

Reference 10

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Observation 381cb235-4979-4727-8cef-4f17d174c890 · outbound

This paper cites Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey,

Reference 11

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This paper cites Summary and reflections on pedestrian trajectory prediction in the field of autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Summary and reflections on pedestrian trajectory prediction in the field of autonomous driving,

Reference 12

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This paper cites A review on explainability in multimodal deep neural nets,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance A review on explainability in multimodal deep neural nets,

Reference 13

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This paper cites Large language models and multimodal foundation models for precision oncology,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Large language models and multimodal foundation models for precision oncology,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.584036Z

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This paper cites Language models are few-shot learners,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Language models are few-shot learners,

Reference 15

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Observation 1ce04a17-3c58-4367-ad62-395bb2c71e54 · outbound

This paper cites Generative AI for Autonomous Driving: Frontiers and Opportunities.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Generative AI for Autonomous Driving: Frontiers and Opportunities

Reference 16

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Observation fb8c95b6-fbc2-4b1a-ae8f-3ae2bb9f47e4 · outbound

This paper cites Frontiers of emerging ai technologies best practices and workforce develop- ment in transportation: Nsf ai–transportation workshop phase ii,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Frontiers of emerging ai technologies best practices and workforce develop- ment in transportation: Nsf ai–transportation workshop phase ii,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.560119Z

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Observation d0d82861-c0b5-4492-9690-875d2632a9cd · outbound

This paper cites Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Virtual Roads, Smarter Safety: A Digital Twin Framework for Mixed Autonomous Traffic Safety Analysis

Reference 18

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Observation 5ab7640e-addd-4be7-b2f4-2910d7d78817 · outbound

This paper cites Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

Reference 19

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This paper cites A self-supervised multi-agent large language model framework for customized traffic mobility analysis using machine learning models,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance A self-supervised multi-agent large language model framework for customized traffic mobility analysis using machine learning models,

Reference 20

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Observation aabcd771-d3cf-46e2-856c-f9ac3b103457 · outbound

This paper cites Vision-language models in remote sensing: Current progress and future trends,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Vision-language models in remote sensing: Current progress and future trends,

Reference 21

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Observation 6460ee49-033c-4ed3-ac1c-fbb9ca9201cb · outbound

This paper cites V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Reference 22

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This paper cites V2x-unipool: Unifying multimodal perception and knowledge reasoning for autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance V2x-unipool: Unifying multimodal perception and knowledge reasoning for autonomous driving,

Reference 23

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This paper cites Improved zero-shot classification by adapting vlms with text descriptions,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Improved zero-shot classification by adapting vlms with text descriptions,

Reference 24

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This paper cites Ez-hoi: Vlm adaptation via guided prompt learning for zero-shot hoi detection,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Ez-hoi: Vlm adaptation via guided prompt learning for zero-shot hoi detection,

Reference 25

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This paper cites Interpreting black-box models: a review on explainable artificial intelligence,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Interpreting black-box models: a review on explainable artificial intelligence,

Reference 26

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Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Explainable ai: A brief survey on history, research areas, approaches and challenges,

Reference 27

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This paper cites Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles,

Reference 28

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This paper cites Deepedgebench: Benchmarking deep neural networks on edge devices,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Deepedgebench: Benchmarking deep neural networks on edge devices,

Reference 29

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Observation 4393ea69-39b8-49d4-94d4-9ed1f80b145a · outbound

This paper cites Prompt engineering in large language models,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Prompt engineering in large language models,

Reference 30

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This paper cites An overview of domain-specific foundation model: key technologies, applications and challenges,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance An overview of domain-specific foundation model: key technologies, applications and challenges,

Reference 31

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This paper cites Delving into multi-modal multi-task foundation models for road scene understanding: From learning paradigm perspectives,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Delving into multi-modal multi-task foundation models for road scene understanding: From learning paradigm perspectives,

Reference 32

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This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 33

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This paper cites Drivelm: Driving with graph visual question answering,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Drivelm: Driving with graph visual question answering,

Reference 34

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This paper cites Autotrust: Benchmark- ing trustworthiness in large vision language models for autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Autotrust: Benchmark- ing trustworthiness in large vision language models for autonomous driving,

Reference 35

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This paper cites Scvlm: Enhancing vision-language model for safety-critical event understanding,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Scvlm: Enhancing vision-language model for safety-critical event understanding,

Reference 36

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This paper cites He-drive: Human-like end-to-end driving with vision language models,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance He-drive: Human-like end-to-end driving with vision language models,

Reference 37

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This paper cites VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision

Reference 38

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Observation afe5fbb6-a97a-487b-990e-deb4e8474cb7 · outbound

This paper cites Vlm-e2e: Enhancing end-to-end autonomous driving with multimodal driver attention fusion,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Vlm-e2e: Enhancing end-to-end autonomous driving with multimodal driver attention fusion,

Reference 39

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This paper cites VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving

Reference 40

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This paper cites RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving

Reference 41

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This paper cites EMMA: End-to-End Multimodal Model for Autonomous Driving.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance EMMA: End-to-End Multimodal Model for Autonomous Driving

Reference 42

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Observation 44767a0d-92c6-4724-9b2e-8fb9679b90bb · outbound

This paper cites Edgeshard: Efficient llm inference via collaborative edge computing,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Edgeshard: Efficient llm inference via collaborative edge computing,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.380083Z

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Observation 826ddaac-078b-451d-81c1-cf720ab819cc · outbound

This paper cites An advanced driving agent with the multimodal large language model for autonomous vehicles,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance An advanced driving agent with the multimodal large language model for autonomous vehicles,

Reference 44

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Observation 542999bb-f6ab-4eec-888a-9e798c1e0997 · outbound

This paper cites Autoreward: Closed-loop reward design with large language models for autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Autoreward: Closed-loop reward design with large language models for autonomous driving,

Reference 45

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Observation 30ece904-9459-4eb5-9387-b79dab3a1c24 · outbound

This paper cites Senserag: Constructing environmental knowledge bases with proactive querying for llm-based autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Senserag: Constructing environmental knowledge bases with proactive querying for llm-based autonomous driving,

Reference 46

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Observation 4b5653ce-f024-4e54-8cca-59b1f8b07de4 · outbound

This paper cites AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging

Reference 47

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Observation e594b4db-02eb-4af6-b99b-62a1a9cdfd6b · outbound

This paper cites V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.170051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1428946-589a-419b-ad47-8f93b5f5d131 · outbound

This paper cites Deep learning with edge computing: A review,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Deep learning with edge computing: A review,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.325432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0775426-5cc0-4429-aa3b-9879971ea349 · outbound

This paper cites Edgevla: Efficient vision-language-action models,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Edgevla: Efficient vision-language-action models,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.310873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 242129a7-4284-440a-ac71-ac7beccf559f · outbound

This paper cites Edgecloudai: Edge-cloud distributed video analytics,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Edgecloudai: Edge-cloud distributed video analytics,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.296436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a262fef9-e679-4d39-8453-0d8fda1e1cc8 · outbound

This paper cites Generative diffusion-based contract design for efficient ai twin migration in vehicular embodied ai networks,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Generative diffusion-based contract design for efficient ai twin migration in vehicular embodied ai networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.282095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fabf948-7a62-46ae-b886-3219b0a7faec · outbound

This paper cites MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:57:51.489118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation add537f2-5dff-4b98-82af-5c3bd2a3caec · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.195198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb5ae6f-f0ba-420e-ac5c-d1904fbc2810 · outbound

This paper cites MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.199560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e90e2a-2391-46d1-ba25-090cfe54c8e6 · outbound

This paper cites MobileVLM V2: Faster and Stronger Baseline for Vision Language Model.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.203800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0fe5b92-22bf-4db9-9c5c-b1c88049ad99 · outbound

This paper cites Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.207982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a5c8f33-cef1-4817-a5ad-f9c83bf1db80 · outbound

This paper cites AppVLM: A Lightweight Vision Language Model for Online App Control.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance AppVLM: A Lightweight Vision Language Model for Online App Control

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.212487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f12d363c-c98b-4de8-bf75-2479858520b3 · outbound

This paper cites MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.216606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f70c9e8-6284-4479-a245-99ac5f6b3f6a · outbound

This paper cites Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.221104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cdc6225-fd6c-4677-8a63-f222b11d5749 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance End-to-end autonomous driving: Challenges and frontiers,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.267258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 32bf424f-ff60-4da1-9cd2-3a2de32afef4 · outbound

This paper cites Privacy-aware anomaly detection and notification enhancement for vanet based on collaborative intrusion detection system,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Privacy-aware anomaly detection and notification enhancement for vanet based on collaborative intrusion detection system,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.253488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2812a34-391c-4dac-ac18-a9417d2979ab · outbound

This paper cites Towards c-v2x enabled collaborative autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Towards c-v2x enabled collaborative autonomous driving,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.239694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c384a51f-41f1-43fb-bae4-c19a7b105d25 · outbound

This paper cites 3d object detection for autonomous driving: A comprehensive survey,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance 3d object detection for autonomous driving: A comprehensive survey,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.224843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5126c6d-7e01-40bc-88d0-5432eb407e30 · outbound

This paper cites Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.209856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d90a7c0c-5159-463e-93b4-b40b4e60535c · outbound

This paper cites CARLA: An open urban driving simulator,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance CARLA: An open urban driving simulator,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.196284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9f1e66a2-7080-4d5f-bc29-01a7672133ca · outbound

This paper cites Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.182053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84436f13-ef88-44d9-82a4-343827707c86 · outbound

This paper cites Disconet: Shapes learning on disconnected manifolds for 3d editing,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Disconet: Shapes learning on disconnected manifolds for 3d editing,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.167939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 057cb3f9-9568-49b7-84e2-dfb30091a161 · outbound

This paper cites V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:57:52.153657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4892fdec-3923-45bc-a999-e85cf9051a54 · outbound

This paper cites CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.261485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb50c36-18fc-4393-a333-5cf8570cfb2c · outbound

This paper cites AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.265746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bc566e4-763f-4c31-9f2b-8d8eea566cf2 · outbound

This paper cites BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.269914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 994ecad8-4e60-469a-9a26-fadaf8c80b7d · outbound

This paper cites BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View.

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T05:57:51.274097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers