REACT: A Real-Time Edge-AI Based V2X Framework for Accident Avoidance in Autonomous Driving System

• URL: http://arxiv.org/abs/2508.01057v1
• Date: Fri, 01 Aug 2025 20:16:04 GMT
• Title: REACT: A Real-Time Edge-AI Based V2X Framework for Accident Avoidance in Autonomous Driving System
• Authors: Fengze Yang, Bo Yu, Yang Zhou, Xuewen Luo, Zhengzhong Tu, Chenxi Liu,
• Abstract summary: This paper presents REACT, a real-time, V2X-integrated trajectory optimization framework built upon a fine-tuned lightweight VLM.<n>To ensure real-time performance on edge devices, REACT incorporates edge adaptation strategies that reduce model complexity and accelerate inference.<n>ReACT achieves state-of-the-art performance, a 77% collision rate reduction, a 48.2% Video Panoptic Quality (VPQ), and a 0.57-second inference latency on the Jetson AGX Orin.
• Score: 12.513296074529727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collisions caused by human error are the most common type of multi-vehicle crash, highlighting the critical need for autonomous driving (AD) systems to leverage cooperative perception through Vehicle-to-Everything (V2X) communication. This capability extends situational awareness beyond the limitations of onboard sensors. However, current transformer-based V2X frameworks suffer from limited generalization, shallow contextual reasoning, and reliance on mono-modal inputs. Vision-Language Models (VLMs) offer enhanced reasoning and multimodal integration but typically fall short of real-time performance requirements in safety-critical applications. This paper presents REACT, a real-time, V2X-integrated trajectory optimization framework built upon a fine-tuned lightweight VLM. REACT integrates a set of specialized modules that process multimodal inputs into optimized, risk-aware trajectories. To ensure real-time performance on edge devices, REACT incorporates edge adaptation strategies that reduce model complexity and accelerate inference. Evaluated on the DeepAccident benchmark, REACT achieves state-of-the-art performance, a 77% collision rate reduction, a 48.2% Video Panoptic Quality (VPQ), and a 0.57-second inference latency on the Jetson AGX Orin. Ablation studies validate the contribution of each input, module, and edge adaptation strategy. These results demonstrate the feasibility of lightweight VLMs for real-time edge-based cooperative planning and showcase the potential of language-guided contextual reasoning to improve safety and responsiveness in autonomous driving.

Related papers

• Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition [57.698383942708]
  Vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety.<n>We organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning.<n>This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration.
  arXiv  Detail & Related papers  (2025-07-29T09:06:40Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [69.54069477520534]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving [13.181643929201666]
  V2X-UniPool is a unified framework that integrates multimodal Vehicle-to-Everything (V2X) data into a time-indexed and language-based knowledge pool.<n>Our system enables ADs to perform accurate, temporally consistent reasoning over both static environment and dynamic traffic context.
  arXiv  Detail & Related papers  (2025-06-03T08:00:57Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving [55.96227460521096]
  Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities.<n>We propose a natural reflection-based backdoor attack targeting VLM systems in autonomous driving scenarios.<n>Our findings uncover a new class of attacks that exploit the stringent real-time requirements of autonomous driving.
  arXiv  Detail & Related papers  (2025-05-09T20:28:17Z)
• FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback [15.55944950850973]
  FASIONAD is a novel dual-system framework that synergizes a fast end-to-end planner with a VLM-based reasoning module.<n>In open-loop experiments, FASIONAD achieves a $6.7%$ reduction in average $L2$ trajectory error and $28.1%$ lower collision rate.
  arXiv  Detail & Related papers  (2025-03-11T08:27:01Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• Multi-Modality Driven LoRA for Adverse Condition Depth Estimation [61.525312117638116]
  We propose Multi-Modality Driven LoRA (MMD-LoRA) for Adverse Condition Depth Estimation.<n>It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning (VTCCL)<n>It achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets.
  arXiv  Detail & Related papers  (2024-12-28T14:23:58Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models [13.716889927164383]
  Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving.<n>This paper introduces V2X-VLM, a novel end-to-end (E2E) cooperative autonomous driving framework based on vision-language models (VLMs)<n>V2X-VLM integrates multiperspective camera views from vehicles and infrastructure with text-based scene descriptions to enable a more comprehensive understanding of driving environments.
  arXiv  Detail & Related papers  (2024-08-17T16:42:13Z)
• Unified End-to-End V2X Cooperative Autonomous Driving [21.631099800753795]
  UniE2EV2X is a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.
  arXiv  Detail & Related papers  (2024-05-07T03:01:40Z)
• NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception [7.819255257787961]
  We introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures. To demonstrate the effectiveness of our solution, we design a new simulation framework to consider autonomous driving, sensor fusion and V2X communication in general.
  arXiv  Detail & Related papers  (2023-07-13T08:33:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.