How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving

• URL: http://arxiv.org/abs/2508.00888v3
• Date: Thu, 02 Oct 2025 10:00:27 GMT
• Title: How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving
• Authors: Amir Hossein Kalantari, Eleonora Papadimitriou, Arkady Zgonnikov, Amir Pooyan Afghari,
• Abstract summary: We propose a unified, context-aware framework that adapts labels and models over time and across drivers.<n>The framework is tested using two safety indicators, speed-weighted headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN)<n>Overall, the framework shows promise for adaptive, context-aware risk detection that can enhance real-time safety feedback and support driver-focused interventions in intelligent transportation systems.
• Score: 0.6299766708197883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable risk identification based on driver behavior data underpins real-time safety feedback, fleet risk management, and evaluation of driver-assist systems. While naturalistic driving studies have become foundational for providing real-world driver behavior data, the existing frameworks for identifying risk based on such data have two fundamental limitations: (i) they rely on predefined time windows and fixed thresholds to disentangle risky and normal driving behavior, and (ii) they assume behavior is stationary across drivers and time, ignoring heterogeneity and temporal drift. In practice, these limitations can lead to timing errors and miscalibration in alerts, weak generalization to new drivers/routes/conditions, and higher false-alarm and miss rates, undermining driver trust and reducing safety intervention effectiveness. To address this gap, we propose a unified, context-aware framework that adapts labels and models over time and across drivers via rolling windows, joint optimization, dynamic calibration, and model fusion, tailored for time-stamped kinematic data. The framework is tested using two safety indicators, speed-weighted headway and harsh driving events, and three models: Random Forest, XGBoost, and Deep Neural Network (DNN). Speed-weighted headway yielded more stable and context-sensitive classifications than harsh-event counts. XGBoost maintained consistent performance under changing thresholds, whereas DNN achieved higher recall at lower thresholds but with greater variability across trials. The ensemble aggregated signals from multiple models into a single risk decision, balancing responsiveness to risky behavior with control of false alerts. Overall, the framework shows promise for adaptive, context-aware risk detection that can enhance real-time safety feedback and support driver-focused interventions in intelligent transportation systems.

Related papers

• Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models [62.16655896700062]
  Activation steering is a technique to enhance the utility of Large Language Models (LLMs)<n>We show that it unintentionally introduces critical and under-explored safety risks.<n>Experiments reveal that these interventions act as a force multiplier, creating new vulnerabilities to jailbreaks and increasing attack success rates to over 80% on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-03T12:32:35Z)
• Controllable risk scenario generation from human crash data for autonomous vehicle testing [13.3074428571403]
  Controllable Risk Agent Generation (CRAG) is a framework designed to unify the modeling of dominant nominal behaviors and rare safety-critical behaviors.<n>CRAG constructs a structured latent space that disentangles normal and risk-related behaviors, enabling efficient use of limited crash data.
  arXiv  Detail & Related papers  (2025-11-27T04:53:18Z)
• Learning collision risk proactively from naturalistic driving data at scale [3.1457219084519004]
  This study introduces the Generalised Surrogate Safety Measure (GSSM)<n>GSSM learns collision risk from naturalistic driving without the need for crash or risk labels.<n> Diverse data from naturalistic driving, including motion kinematics, weather, lighting, etc., are used to train multiple GSSMs.<n>A basic GSSM using only instantaneous motion kinematics achieves an area under the precision-recall curve of 0.9 and secures a median time advance of 2.6 seconds to prevent potential collisions.
  arXiv  Detail & Related papers  (2025-05-19T07:22:32Z)
• 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)
• RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion [17.46462636610847]
  Risk- Driving Environment (RADE) is a simulation framework that generates statistically realistic and risk-adjustable traffic scenes.<n>RADE learns risk-conditioned behaviors directly from data, preserving naturalistic multi-agent interactions with controllable risk levels.<n>We validate RADE on the real-world rounD dataset, demonstrating that it preserves statistical realism across varying risk levels.
  arXiv  Detail & Related papers  (2025-05-06T04:41:20Z)
• Predicting Driver's Perceived Risk: a Model Based on Semi-Supervised Learning Strategy [7.227510169013427]
  Driver's subjective perceived risk (DSPR) model is proposed, regarding perceived risk as a dynamically triggered mechanism with anisotropy and attenuation.<n>20 participants are recruited for a driver-in-the-loop experiment to report their real-time subjective risk ratings (SRRs) when experiencing various automatic driving scenarios.<n>DSPR achieves the highest prediction accuracy of 87.91% in predicting SRRs, compared to three state-of-the-art risk models.
  arXiv  Detail & Related papers  (2025-04-17T05:50:33Z)
• SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models [63.71984266104757]
  We propose SafeAuto, a framework that enhances MLLM-based autonomous driving by incorporating both unstructured and structured knowledge.<n>To explicitly integrate safety knowledge, we develop a reasoning component that translates traffic rules into first-order logic.<n>Our Multimodal Retrieval-Augmented Generation model leverages video, control signals, and environmental attributes to learn from past driving experiences.
  arXiv  Detail & Related papers  (2025-02-28T21:53:47Z)
• Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights [18.92479778025183]
  In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers.<n>Previous models fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction.<n>We introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms.
  arXiv  Detail & Related papers  (2025-02-27T13:43:17Z)
• INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation [7.362380225654904]
  INSIGHT is a hierarchical vision-language model (VLM) framework designed to enhance hazard detection and edge-case evaluation.<n>By using multimodal data fusion, our approach integrates semantic and visual representations, enabling precise interpretation of driving scenarios.<n> Experimental results on the BDD100K dataset demonstrate a substantial improvement in hazard prediction straightforwardness and accuracy over existing models.
  arXiv  Detail & Related papers  (2025-02-01T01:43:53Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification [23.05821759499963]
  Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. We propose DSDFormer, a framework that integrates the strengths of Transformer and Mamba architectures. We also introduce Temporal Reasoning Confident Learning (TRCL), an unsupervised approach that refines noisy labels by leveragingtemporal correlations in video.
  arXiv  Detail & Related papers  (2024-09-09T13:16:15Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [46.909086734963665]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.<n>Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.<n> RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [50.936478241688114]
  Nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods.<n>We propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure.<n>Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• Driving Anomaly Detection Using Conditional Generative Adversarial Network [26.45460503638333]
  This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN) The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals. The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores.
  arXiv  Detail & Related papers  (2022-03-15T22:10:01Z)
• Sample-Efficient Safety Assurances using Conformal Prediction [57.92013073974406]
  Early warning systems can provide alerts when an unsafe situation is imminent. To reliably improve safety, these warning systems should have a provable false negative rate. We present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics.
  arXiv  Detail & Related papers  (2021-09-28T23:00:30Z)
• Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning [0.0]
  We propose a two-step approach for risk-sensitive behavior generation for self-driving vehicles. First, we learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning. During execution, the optimal risk-sensitive action is selected by applying established risk criteria.
  arXiv  Detail & Related papers  (2021-02-05T11:45:12Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

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.