A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

• URL: http://arxiv.org/abs/2008.11226v1
• Date: Tue, 25 Aug 2020 18:21:35 GMT
• Title: A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis
• Authors: Ce Zhang, Azim Eskandarian
• Abstract summary: EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications.
• Score: 164.93739293097605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drivers cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.

Related papers

• Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving [12.765198683804094]
  Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities annually attributed to traffic accidents. We propose a novel multi-task DMS, termed VDMoE, which leverages RGB video input to monitor driver states non-invasively.
  arXiv  Detail & Related papers  (2024-10-28T14:49:18Z)
• DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving [65.04871316921327]
  This paper introduces a new autonomous driving system that enhances the performance and reliability of autonomous driving system. DME-Driver utilizes a powerful vision language model as the decision-maker and a planning-oriented perception model as the control signal generator. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.
  arXiv  Detail & Related papers  (2024-01-08T03:06:02Z)
• G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving [71.9040410238973]
  We focus on inferring the ego trajectory of a driver's vehicle using their gaze data. Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data. The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• Visual Saliency Detection in Advanced Driver Assistance Systems [7.455416595124159]
  We present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. We employ an innovative biosensor embedded on the car steering wheel to monitor the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series.
  arXiv  Detail & Related papers  (2023-07-26T15:41:54Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• 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)
• 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)
• A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge [49.976633450740145]
  This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
  arXiv  Detail & Related papers  (2020-10-23T18:07:48Z)
• Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm [8.356765961526955]
  This work is to extend the driver drowsiness detection in vehicles using signals of a driver monitoring camera. For this purpose, 35 features related to the driver's eye blinking behavior and head movements are extracted in driving simulator experiments. A concluding analysis of the best performing feature sets yields valuable insights about the influence of drowsiness on the driver's blink behavior and head movements.
  arXiv  Detail & Related papers  (2020-09-28T12:37:38Z)
• Improved YOLOv3 Object Classification in Intelligent Transportation System [29.002873450422083]
  An algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway. The model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.
  arXiv  Detail & Related papers  (2020-04-08T11:45:13Z)

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.