On creating vision‐based advanced driver assistance systems

Nieto, Marcos; Otaegui, Oihana; Vélez, Gorka; Ortega, Juan Diego; Cortés, Andoni

doi:10.1049/iet-its.2013.0167

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…The sequential processing is obtained at the high level which is responsible for decision making [10]. In addition to [10], [11] presents the embedded vision challenges regarding the hardware and software issues such as power consumption, timing etc. From, the literature, it can be observed that object recognition, detection and tracking has been demanding in real time.…”

Section: Related Workmentioning

confidence: 99%

Online Object Trajectory Classification Using FPGA-SoC Devices

Shinde

Machado

Santos

et al. 2018

Advances in Intelligent Systems and Computing

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Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC-FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.

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Section: Related Workmentioning

confidence: 99%

Online Object Trajectory Classification Using FPGA-SoC Devices

Shinde

Machado

Santos

et al. 2018

Advances in Intelligent Systems and Computing

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show abstract

“…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%

“…Therefore, there must be a trade-off among these design requisites [217]. Moreover, there is not a commonly accepted hardware and software platform, so different solutions have been proposed by the industry and the scientific community.…”

Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

105

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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 reason behind this introductory level of automation and subsequent driver's responsibilities, is the complexity for an automated system to interpret or understand many contextual factors relevant to safe driving. Despite enormous technological achievements when introducing these systems, developers need to account for technological boundary conditions (Flemisch et al, 2014;Nieto, Otaegui, Vélez, Ortega, & Cortés, 2015). In fact, Nieto et al (2015) indicated reliability of hardware and software as well as resistance to harsh conditions some of the key challenges for development of ADAS' sensors.…”

Section: Improvement Potentialmentioning

confidence: 99%

“…Despite enormous technological achievements when introducing these systems, developers need to account for technological boundary conditions (Flemisch et al, 2014;Nieto, Otaegui, Vélez, Ortega, & Cortés, 2015). In fact, Nieto et al (2015) indicated reliability of hardware and software as well as resistance to harsh conditions some of the key challenges for development of ADAS' sensors. The definition of partial automation assumes drivers are available at all time to take over control of the vehicle if required (De Winter et al, 2014;Gold et al, 2013).…”

Section: Improvement Potentialmentioning

confidence: 99%

Driving automation interface design : supporting drivers' changing role

Beukel¹

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On creating vision‐based advanced driver assistance systems

Cited by 20 publications

References 17 publications

Online Object Trajectory Classification Using FPGA-SoC Devices

Online Object Trajectory Classification Using FPGA-SoC Devices

Driver Distraction Using Visual-Based Sensors and Algorithms

Driving automation interface design : supporting drivers' changing role

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