Embedded System Enabled Vehicle Collision Detection: An ANN Classifier

Katare, Dewant; El-Sharkawy, Mohamed

doi:10.1109/ccwc.2019.8666562

Cited by 22 publications

(19 citation statements)

References 14 publications

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“…It has been tested for processing and fusing the data streams in the realtime or even in the post-processing scenarios. The software architecture consists of several independent modules that can be used for different situation and circumstance [10] [13].…”

Section: B Rtmapsmentioning

confidence: 99%

“…RTMaps Component Library: : It consists of the software module which can be easily interfaced with the automotive and other related sensors and packages such as Python, C++, Simulink models and 3-d viewers. responsible for the development of an application [10] [13].…”

Section: B Rtmapsmentioning

confidence: 99%

“…RTMaps Embedded: : It is a framework which comprises of the component library and the runtime engine with the capability of running on an embedded x86 or ARM capable platform such as NXP Bluebox, Raspberry Pi, DSpace MicroAutobox [10] [13].…”

Section: B Rtmapsmentioning

confidence: 99%

“…The highest level of autonomy in a vehicle can be described as execution of multiple processes, that serves the self driving functionality from a source point to destination point without any input or control to a vehicle from the human. Research shows, this highest level of autonomy is achieved by integrating multiple sensors such as camera, lidar, global navigation satellite system, radar which provides the automotive safety feature or the advanced driver assistance system [5] [13] [18]. The essential component or sub-system of an autonomous or self-driving vehicle can be specified into four categories: sensing, perception, planning and control.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)

Katare

El-Sharkawy

2019

2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

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An Autonomous vehicle or present day smart vehicle is equipped with several ADAS safety features such as Blind Spot Detection, Forward Collision Warning, Lane Departure and Parking Assistance, Surround View System, Vehicular communication System. Recent research utilize deep learning algorithms as a counterfeit for these traditional methods, using optimal sensors. This paper discusses the perception tasks related to autonomous vehicle, specifically the computer-vision approach of 3D object detection and thus proposes a model compatible with embedded system using the RTMaps framework. The proposed model is based on the sensors: camera and Lidar connected to an autonomous embedded system, providing the sensed inputs to the deep learning classifier which on the basis of theses inputs estimates the position and predicts a 3-d bounding box on the physical objects. The Frustum PointNet a contemporary architecture for 3-D object detection is used as base model and is implemented with extended functionality. The architecture is trained and tested on the KITTI dataset and is discussed with the competitive validation precision and accuracy. The Presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.

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Section: B Rtmapsmentioning

confidence: 99%

Section: B Rtmapsmentioning

confidence: 99%

Section: B Rtmapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)

Katare

El-Sharkawy

2019

2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

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“…In addition, Victoria Transport Policy Institute quoted that this technology will be widely used after 2040–2050 [ 7 ]. Currently, the most advanced features found in autonomous vehicles are Lane Changing (LC) Control [ 8 , 9 ], Adaptive Cruise Control (ACC) [ 10 ], Automatic Emergency Braking System (AEBS) [ 11 ], Light Detection and Ranging (LIDAR) [ 12 ], Street Sign Recognition [ 13 ], Vehicle to Vehicle (V2V) Communication [ 14 ], Object or Collision Avoidance System (CAS) [ 15 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Haris

Hou

2020

Sensors

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Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

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Cybersecurity System with State Observer and K-Means Clustering Machine Learning Model

Sagdatullin¹

2023

Communications in Computer and Information Science

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Embedded System Enabled Vehicle Collision Detection: An ANN Classifier

Cited by 22 publications

References 14 publications

Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)

Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)

Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Cybersecurity System with State Observer and K-Means Clustering Machine Learning Model

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