Driver Monitoring Based on Low-Cost 3-D Sensors

Peláez, A C Gustavo; García, Fernando; Escalera, Arturo de la; Armingol, José María

doi:10.1109/tits.2014.2332613

Cited by 34 publications

(12 citation statements)

References 26 publications

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“…The crash shapefile should contain the geometric info (x-coordinate, y-coordinate) that is used to locate crashes on intersections. Crash-related factors [41,42], such as environment and driver, and crash attributes, such as crash type and date, are not necessary but are suggested.…”

Section: Data Typesmentioning

confidence: 99%

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

2020

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In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis–Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008–2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs.

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Section: Data Typesmentioning

confidence: 99%

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

2020

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“…The implementation of computer vision applications in automotive environments is not straightforward because several requirements must be taken into account: reliability [212,213], real-time performance [213,214,215], low-cost [216,217,218,219], spatial constraints [217,219], low power consumption [220], flexibility [219], rapid prototyping [215,221], design requirements [217] and short time to market [217]. Therefore, there must be a trade-off among these design requisites [217].…”

Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…The first set of works achieves driver monitoring by the use of external data acquisition devices that provide further information to the system. In the work of Pelaez et al [ 2 ], driver gaze is identified based on a low cost sensor (the Kinect from Microsoft), and 3D point cloud matching based on the Iterative Closest Point (ICP) method. In the work presented by Heo and Savvides [ 3 ], two cameras are used to provide frontal and profile accurate 3D face modelling and 2D pose synthesis.…”

Section: Previous Workmentioning

confidence: 99%

Data Fusion for Driver Behaviour Analysis

Carmona

García

Martín

et al. 2015

Sensors

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A driver behaviour analysis tool is presented. The proposal offers a novel contribution based on low-cost hardware and advanced software capabilities based on data fusion. The device takes advantage of the information provided by the in-vehicle sensors using Controller Area Network Bus (CAN-BUS), an Inertial Measurement Unit (IMU) and a GPS. By fusing this information, the system can infer the behaviour of the driver, providing aggressive behaviour detection. By means of accurate GPS-based localization, the system is able to add context information, such as digital map information, speed limits, etc. Several parameters and signals are taken into account, both in the temporal and frequency domains, to provide real time behaviour detection. The system was tested in urban, interurban and highways scenarios.

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Driver Monitoring Based on Low-Cost 3-D Sensors

Cited by 34 publications

References 26 publications

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

Driver Distraction Using Visual-Based Sensors and Algorithms

Data Fusion for Driver Behaviour Analysis

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