Detecting Anomalous Driving Behavior using Neural Networks

Matousek, Matthias; El-Zohairy, Mohamed; Al-Momani, Ala'a; Kargl, Frank; Bösch, Christoph

doi:10.1109/ivs.2019.8814246

Cited by 27 publications

(12 citation statements)

References 9 publications

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“…The model achieves a 97.10% Area Under the Curve (AUC). While in the work [57], they presented a significantly improved anomaly detection mechanism using Recurrent Neural Networks (RNNs) based on simulator data. This method achieves 78.6% precision and 36.4% recall.…”

Section: B Aggressive Driver Behaviormentioning

confidence: 99%

Driver Behavior Classification: A Systematic Literature Review

2023

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Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of the driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of the classification of driver behavior. The study aim is to highlight and analyze the different types of driver behavior, data sources, datasets, features, and artificial intelligence techniques used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.INDEX TERMS Driver behavior, intelligent transport system, systematic literature review, machine learning, deep learning.

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Section: B Aggressive Driver Behaviormentioning

confidence: 99%

Driver Behavior Classification: A Systematic Literature Review

2023

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“…Driving behavior prediction is another approach to identify anomalies. To give an example, authors in [7] (RNN) and a long short-term memory (LSTM) to predict driver's actions, and mark behaviors that are varying from the predicted ones.…”

Section: Related Workmentioning

confidence: 99%

“…Existing solutions such as [3]- [7] use a dataset of normal driving patterns, and mark any unseen pattern as an anomaly. However, several factors like weather condition, road side constructions and traffic load can change the behavior of vehicles.…”

Section: Introductionmentioning

confidence: 99%

DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks

Sabour¹,

Rao²,

Ghaderi³

2021

Preprint

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Nowadays, many cities are equipped with surveillance systems and traffic control centers to monitor vehicular traffic for road safety and efficiency. The monitoring process is mostly done manually which is inefficient and expensive. In recent years, several data-driven solutions have been proposed in the literature to automatically analyze traffic flow data using machine learning techniques. However, existing solutions require large and comprehensive datasets for training which are not readily available, thus limiting their application. In this paper, we develop a traffic anomaly detection system, referred to as DeepFlow, based on Siamese neural networks, which are suitable in scenarios where only small datasets are available for training. Our model can detect abnormal traffic flows by analyzing the trajectory data collected from the vehicles in a fleet. To evaluate DeepFlow, we use realistic vehicular traffic simulations in SUMO. Our results show that DeepFlow detects abnormal traffic patterns with an F1 score of 78%, while outperforming other existing approaches including: Dynamic Time Warping (DTW), Global Alignment Kernels (GAK), and iForest.

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“…M. Matousek et al [109] have proposed a neural networkbased aggressive driver behavior detection technique. The proposed technique employed LSTM and RNNs (autoencoding Replicator Neural Networks) for driver behavior detection using pedals, steering wheels and most recent history of the vehicle.…”

Section: Comparative Study Of Driver Aggressiveness Detection Techmentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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Detecting Anomalous Driving Behavior using Neural Networks

Cited by 27 publications

References 9 publications

Driver Behavior Classification: A Systematic Literature Review

Driver Behavior Classification: A Systematic Literature Review

DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

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