Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark

• URL: http://arxiv.org/abs/2506.03381v1
• Date: Tue, 03 Jun 2025 20:40:44 GMT
• Title: Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmark
• Abstract summary: We propose a novel Incident Response Benchmark that uses generative artificial intelligence to automatically generate response plans for incoming traffic incidents.<n>Our approach aims to significantly reduce incident resolution times by suggesting context-appropriate actions such as variable message sign deployment, lane closures, and emergency resource allocation adapted to specific incident characteristics.
• Score: 12.896575987798464
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traffic incidents remain a critical public safety concern worldwide, with Australia recording 1,300 road fatalities in 2024, which is the highest toll in 12 years. Similarly, the United States reports approximately 6 million crashes annually, raising significant challenges in terms of a fast reponse time and operational management. Traditional response protocols rely on human decision-making, which introduces potential inconsistencies and delays during critical moments when every minute impacts both safety outcomes and network performance. To address this issue, we propose a novel Incident Response Benchmark that uses generative artificial intelligence to automatically generate response plans for incoming traffic incidents. Our approach aims to significantly reduce incident resolution times by suggesting context-appropriate actions such as variable message sign deployment, lane closures, and emergency resource allocation adapted to specific incident characteristics. First, the proposed methodology uses real-world incident reports from the Performance Measurement System (PeMS) as training and evaluation data. We extract historically implemented actions from these reports and compare them against AI-generated response plans that suggest specific actions, such as lane closures, variable message sign announcements, and/or dispatching appropriate emergency resources. Second, model evaluations reveal that advanced generative AI models like GPT-4o and Grok 2 achieve superior alignment with expert solutions, demonstrated by minimized Hamming distances (averaging 2.96-2.98) and low weighted differences (approximately 0.27-0.28). Conversely, while Gemini 1.5 Pro records the lowest count of missed actions, its extremely high number of unnecessary actions (1547 compared to 225 for GPT-4o) indicates an over-triggering strategy that reduces the overall plan efficiency.

Related papers

• Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations [55.251494694783894]
  Disaster Operational Response Agent benchmark (DORA) is the first agentic benchmark for end-to-end disaster response.<n>Tasks span five dimensions that cover the operational disaster-response pipeline: disaster perception, spatial relational analysis, rescue and evacuation planning, temporal evolution reasoning, and multi-modal report synthesis.<n>DORA establishes a rigorous testbed for operationally reliable disaster-response agents.
  arXiv  Detail & Related papers  (2026-05-12T06:57:41Z)
• Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence [0.09558392439655013]
  This paper introduces a novel open-source intelligence (OSINT) methodology for detecting real-world scheming incidents.<n>Analysing over 183,420 transcripts from X (formerly Twitter), we identify 698 real-world scheming-related incidents between October 2025 and March 2026.<n>We find evidence of multiple scheming-related behaviours in real-world deployments previously reported only in experiments.
  arXiv  Detail & Related papers  (2026-04-10T08:37:18Z)
• World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry [82.93104394404781]
  General-purpose world models promise scalable policy evaluation, optimization, and planning.<n>We propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve.
  arXiv  Detail & Related papers  (2026-04-02T12:48:36Z)
• CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving [0.3823356975862005]
  This work examines real-world AV incidents reported in the NHTSA database.<n>We introduce CRASH, an agent that automates reasoning over crash reports by leveraging both standardized fields and unstructured narrative descriptions.<n>Our findings show that CRASH attributes 64% of incidents to perception or planning failures, underscoring the importance of reasoning-based analysis for accurate fault attribution.<n>We further validate CRASH with five domain experts, achieving 86% accuracy in attributing AV system failures.
  arXiv  Detail & Related papers  (2026-03-16T14:39:33Z)
• A Lightweight LLM Framework for Disaster Humanitarian Information Classification [0.0]
  This paper develops a lightweight, cost-effective framework for disaster tweet classification using parameter-efficient fine-tuning.<n>We construct a unified experimental corpus by integrating and normalizing the HumAID dataset.<n>We demonstrate that LoRA achieves 79.62% humanitarian classification accuracy (+37.79% over zero-shot) while training only 2% of parameters.
  arXiv  Detail & Related papers  (2026-01-21T02:05:25Z)
• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail [85.47497935739936]
  Alpamayo-R1 (AR1) is a vision-language-action model that integrates Chain of Causation reasoning with trajectory planning.<n>We show AR1 achieves 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline.<n>We plan to release AR1 models and a subset of the CoC in a future update.
  arXiv  Detail & Related papers  (2025-10-30T01:25:34Z)
• UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles [79.10221881250759]
  Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP) is a vision-language model-based planning approach.<n>It enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making.<n> Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin.
  arXiv  Detail & Related papers  (2025-10-14T21:09:09Z)
• An Automated Attack Investigation Approach Leveraging Threat-Knowledge-Augmented Large Language Models [17.220143037047627]
  Advanced Persistent Threats (APTs) compromise high-value systems to steal data or disrupt operations.<n>Existing methods suffer from poor platform generality, limited generalization to evolving tactics, and an inability to produce analyst-ready reports.<n>We present an LLM-empowered attack investigation framework augmented with a dynamically adaptable Kill-Chain-aligned threat knowledge base.
  arXiv  Detail & Related papers  (2025-09-01T08:57:01Z)
• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces [59.80143393787701]
  Large language models (LLMs) can handle uncertainty and promise to accelerate replanning while lowering the barrier to entry.<n>We introduce a neurosymbolic framework that pairs the accessibility of natural-language dialogue with verifiable guarantees on goal interpretation.<n>A lightweight model, fine-tuned on just 100 uncertainty-filtered examples, surpasses the zero-shot performance of GPT-4.1 while cutting inference latency by nearly 50%.
  arXiv  Detail & Related papers  (2025-07-15T14:24:01Z)
• IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence [2.1711205684359247]
  IncidentResponseGPT is a novel system that applies generative artificial intelligence (AI) to traffic incident response. It generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities.
  arXiv  Detail & Related papers  (2024-04-29T09:45:46Z)
• Auto311: A Confidence-guided Automated System for Non-emergency Calls [2.025468874117372]
  We analyzed 11,796 non-emergency call recordings and developed Auto311, the first automated system to handle 311 non-emergency calls. We used real-world data to evaluate the system's effectiveness and deployability.
  arXiv  Detail & Related papers  (2023-12-19T20:52:04Z)
• Artificial Intelligence for Emergency Response [0.6091702876917281]
  Emergency response management (ERM) is a challenge faced by communities across the globe. Data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch.
  arXiv  Detail & Related papers  (2023-06-15T18:16:08Z)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark [77.54411007883962]
  We propose a Cognitive Accident Prediction (CAP) method that explicitly leverages human-inspired cognition of text description on the visual observation and the driver attention to facilitate model training. CAP is formulated by an attentive text-to-vision shift fusion module, an attentive scene context transfer module, and the driver attention guided accident prediction module. We construct a new large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames.
  arXiv  Detail & Related papers  (2022-12-19T11:43:02Z)
• Designing Decision Support Systems for Emergency Response: Challenges and Opportunities [3.8532022064807827]
  Emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with community partners.
  arXiv  Detail & Related papers  (2022-02-23T02:02:32Z)
• Deep Structured Reactive Planning [94.92994828905984]
  We propose a novel data-driven, reactive planning objective for self-driving vehicles. We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
  arXiv  Detail & Related papers  (2021-01-18T01:43:36Z)
• Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition [79.50450766097686]
  This paper proposes the targeted attention attack (TAA) method for real world road sign attack. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method.
  arXiv  Detail & Related papers  (2020-10-09T02:31:34Z)
• On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities [21.22596396400625]
  Emergency Response Management (ERM) is a critical problem faced by communities across the globe. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. We propose two partially decentralized multi-agent planning algorithms that utilizes and exploit the structure of the dispatch problem.
  arXiv  Detail & Related papers  (2020-01-21T07:04:38Z)

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.