A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion

Ziębiński, Adam; Cupek, Rafał; Erdogan, Hueseyin; Waechter, Sonja

doi:10.1007/978-3-319-45246-3_13

Cited by 128 publications

(66 citation statements)

References 18 publications

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“…Detection devices are being integrated with various equipment for supplementary tasks such as driver drowsiness and lane departure detection, primarily by automobile and component manufacturers. The development of a driver monitoring technology that captures, recognizes, and judges driver behavior efficiently through parameters such as steering wheel operation is also advancing [20][21][22][23][24]. When driving a vehicle, a driver performs basic operations such as adjusting the steering wheel, pushing the accelerator pedal, and checking the mirror.…”

Section: Predicting Driver Drowsinessmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

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Section: Predicting Driver Drowsinessmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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“…The pseudocode to execute the regional collision association is shown in Algorithm 1. [3][3] then 4: return false 5: return true In order to quantify the degree of association between two local tracks, the concept of the Jaccard coefficient is cited. The Jaccard coefficient refers to the ratio between the intersection and union of the two sets.…”

Section: Regional Collision Associationmentioning

confidence: 99%

“…Currently, the sensors commonly used for target detection on the market include lidar, millimeter-wave (MMW) radar, camera, and ultrasonic radar [3]. Among them, MMW radar can work in all weather, and the detection of distance and speed is relatively accurate.…”

Section: Introductionmentioning

confidence: 99%

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Chen

Pei

Chen

2019

Sensors

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Accurate target detection is the basis of normal driving for intelligent vehicles. However, the sensors currently used for target detection have types of defects at the perception level, which can be compensated by sensor fusion technology. In this paper, the application of sensor fusion technology in intelligent vehicle target detection is studied with a millimeter-wave (MMW) radar and a camera. The target level fusion hierarchy is adopted, and the fusion algorithm is divided into two tracking processing modules and one fusion center module based on the distributed structure. The measurement information output by two sensors enters the tracking processing module, and after processing by a multi-target tracking algorithm, the local tracks are generated and transmitted to the fusion center module. In the fusion center module, a two-level association structure is designed based on regional collision association and weighted track association. The association between two sensors’ local tracks is completed, and a non-reset federated filter is used to estimate the state of the fusion tracks. The experimental results indicate that the proposed algorithm can complete a tracks association between the MMW radar and camera, and the fusion track state estimation method has an excellent performance.

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“…Environment perception is one of the key enablers of advanced driver assistance systems in vehicles and especially on the way to autonomous driving. Different types of sensors for environment perception can be found on board of vehicles (Winner et al, 2015) (Ziebinski et al, 2016). Ultrasonic, radar, LiDAR sensors and cameras are together covering applications ranging from centimeters till few hundred meters, like parking assistance (Zhang et al, 2014), object detection or adaptive cruise control.…”

Section: Calibration Of On-board Vehicle Camerasmentioning

confidence: 99%

Semantic Road Scene Knowledge for Robust Self-Calibration of Environment-Observing Vehicle Cameras

Hanel

Stilla

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Environment-observing vehicle camera self-calibration using a structure from motion (SfM) algorithm allows calibration over vehicle lifetime without the need of special calibration objects being present in the calibration images. Scene-specific problems with featurebased correspondence search and reconstruction during the SfM pipeline might be caused by critical objects like moving objects, poor-texture objects or reflecting objects and might have negative influence on camera calibration. In this contribution, a method to use semantic road scene knowledge by means of semantic masks for a semantic-guided SfM algorithm is proposed to make the calibration more robust. Semantic masks are used to exclude image parts showing critical objects from feature extraction, whereby semantic knowledge is obtained by semantic segmentation of the road scene images. The proposed method is tested with an image sequence recorded in a suburban road scene. It has been shown that semantic guidance leads to smaller deviations of the estimated interior orientation and distortion parameters from reference values obtained by test field calibration compared to a standard SfM algorithm.

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A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion

Cited by 128 publications

References 18 publications

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Research on Target Detection Based on Distributed Track Fusion for Intelligent Vehicles

Semantic Road Scene Knowledge for Robust Self-Calibration of Environment-Observing Vehicle Cameras

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