Detecting Aggressive Driving Behavior using Mobile Smartphone

Chhabra, Rishu; Verma, Seema; Krishna, C. Rama

doi:10.1007/978-981-13-1217-5_49

Cited by 24 publications

(17 citation statements)

References 25 publications

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“…Therefore, data collected and processed by such sensors could be utilized to deliver a comprehensive description of driving scenarios. A lot of efforts have been made to transfer portable devices to an alternative platform capable of naturalistic driving data collection [29], distraction and drowsy alert [30], driver behavior analyzing [31], [32], aggressive/dangerous driving detection [33], [34] or to provide auxiliary driving guidance [35]. Moreover, researchers also employ such a device's ability to connect to the network, providing services like traffic notification [36], remote diagnostics [37], [38], vehicle-to-vehicle (V2V) communication [39], and other applications in intelligent transportation system field [40].…”

Section: Driving Event Recognition Using Free-positioned Portable Devicementioning

confidence: 99%

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Liu¹,

Hansen

2021

IEEE Open J. Intell. Transp. Syst.

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Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.

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Section: Driving Event Recognition Using Free-positioned Portable Devicementioning

confidence: 99%

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Liu¹,

Hansen

2021

IEEE Open J. Intell. Transp. Syst.

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“…For examples, these could be maneuverings of an aggressive driving style. In [15]- [18], rule-based methods are used for unsafe driving detection. Basically, in these works, an unsafe driving event is identified if the acceleration estimated from the accelerometer satisfies some pre-defined conditions.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Vehicle Maneuvering Detection With Digital Compass

Leakkaw

Panichpapiboon

2021

IEEE Access

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Vehicle maneuverings are important pieces of information for many applications such as traffic incident detection and driving behavior recognition. On a macroscopic level, an unusually large number of some maneuvering events (e.g., lane changes) may suggest that a road incident occurs. On a microscopic level, vehicle maneuverings can tell how each individual person drives (e.g., safely or unsafely). A number of smartphone-based maneuvering detection methods have been proposed. However, most of them typically rely on accelerometer and gyroscope, and thus require the smartphone to be placed at a specific position on a vehicle and in a specific orientation. In this work, we take a rather different approach from most of the existing methods. Particularly, we investigate how effective it is to detect vehicle maneuverings by relying only on a signal from a digital compass on a smartphone. To this end, we introduce a simple rule-based maneuvering detection method which only takes the heading angles measured by the digital compass as inputs. This allows the smartphone to be placed freely at any position on the vehicle and in any orientation. Our results show that the digital compass can be used to detect turns and u-turns extremely well, and its accuracy on lane change detection is at an acceptable level.INDEX TERMS Vehicle maneuvering detection, digital compass, magnetometer, mobile sensing.

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“…Moreover, smartphone reliant driving event characterization systems are also studied in the literature [12] to utilize the cost-efficient in-built sensors of the smartphones. In [13], the authors designed a system to detect sudden changes of acceleration and unsafe vehicle turns in order to categorise the drivers as aggressive or nonaggressive drivers.…”

Section: A Driving Event Detection Systemsmentioning

confidence: 99%

Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data

Taherifard¹,

Şimşek²,

Lascelles³

et al. 2021

Preprint

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Precision in event characterization in connected vehicles has become increasingly important with the responsive connectivity that is available to the modern vehicles. Event characterization via vehicular sensors are utilized in safety and autonomous driving applications in vehicles. While characterization systems have been shown to be capable of predicting the risky driving patterns, precision of such systems still remains an open issue. The major issues against the driving event characterization systems need to be addressed in connected vehicle settings, which are the heavy imbalance and the event infrequency of the driving data and the existence of the time-series detection systems that are optimized for vehicular settings. To overcome the problems, we introduce the application of the prior-knowledge input method to the characterization systems. Furthermore, we propose a recurrent-based denoising auto-encoder network to populate the existing data for a more robust training process. The results of the conducted experiments show that the introduction of knowledgebased modelling enables the existing systems to reach significantly higher accuracy and F1-score levels. Ultimately, the combination of the two methods enables the proposed model to attain 14.7% accuracy boost over the baseline by achieving an accuracy of 0.96.

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Detecting Aggressive Driving Behavior using Mobile Smartphone

Cited by 24 publications

References 25 publications

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Real-Time Vehicle Maneuvering Detection With Digital Compass

Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data

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