Context-Aware Quantitative Risk Assessment Machine Learning Model for Drivers Distraction

• URL: http://arxiv.org/abs/2402.13421v1
• Date: Tue, 20 Feb 2024 23:20:36 GMT
• Title: Context-Aware Quantitative Risk Assessment Machine Learning Model for Drivers Distraction
• Authors: Adebamigbe Fasanmade, Ali H. Al-Bayatti, Jarrad Neil Morden and Fabio Caraffini
• Abstract summary: Multi-Class Driver Distraction Risk Assessment (MDDRA) model considers the vehicle, driver, and environmental data during a journey. MDDRA categorises the driver on a risk matrix as safe, careless, or dangerous. We apply machine learning techniques to classify and predict driver distraction according to severity levels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data during a journey. MDDRA categorises the driver on a risk matrix as safe, careless, or dangerous. It offers flexibility in adjusting the parameters and weights to consider each event on a specific severity level. We collect real-world data using the Field Operation Test (TeleFOT), covering drivers using the same routes in the East Midlands, United Kingdom (UK). The results show that reducing road accidents caused by driver distraction is possible. We also study the correlation between distraction (driver, vehicle, and environment) and the classification severity based on a continuous distraction severity score. Furthermore, we apply machine learning techniques to classify and predict driver distraction according to severity levels to aid the transition of control from the driver to the vehicle (vehicle takeover) when a situation is deemed risky. The Ensemble Bagged Trees algorithm performed best, with an accuracy of 96.2%.

Related papers

• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• On using Machine Learning Algorithms for Motorcycle Collision Detection [0.0]
  Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms.
  arXiv  Detail & Related papers  (2024-03-14T15:32:25Z)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning [1.4699455652461728]
  We propose a warning system that uses human states in the form of driver errors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver's behavior plan can become unsafe.
  arXiv  Detail & Related papers  (2023-06-06T16:39:58Z)
• Infrastructure-based End-to-End Learning and Prevention of Driver Failure [68.0478623315416]
  FailureNet is a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. It can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.
  arXiv  Detail & Related papers  (2023-03-21T22:55:51Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [75.83518507463226]
  Nonobjective driving experience is difficult to model. In this paper, we propose a FeedBack Loop Network (FBLNet) which attempts to model the driving experience accumulation procedure. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios [0.46408356903366527]
  We propose an open evolving framework for generating personalized on-board driving assistance. A tailored CBR-based method is then proposed to retrieve, reuse and revise the existing cases to generate the assistance. We take the 100-Car Naturalistic Driving Study dataset as an example to build and test our framework.
  arXiv  Detail & Related papers  (2022-11-23T09:28:28Z)
• Driver-centric Risk Object Identification [25.85690304998681]
  We propose a driver-centric definition of risk, i.e., risky objects influence driver behavior. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework. A driver-centric Risk Object Identification dataset is curated to evaluate the proposed system.
  arXiv  Detail & Related papers  (2021-06-24T17:27:32Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)
• Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference [19.71459945458985]
  We propose a driver-centric definition of risk, i.e., objects influencing drivers' behavior are risky. We present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model. Our framework achieves a substantial average performance boost over a strong baseline by 7.5%.
  arXiv  Detail & Related papers  (2020-03-05T04:14:35Z)

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.