A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

Safety is one of the central elements of society functioning. The eSafety aims at increasing road safety through the use of intelligent integrated safety systems. This article presents the concept and PCB implementation of an on-board compact eCall device that can be installed at the owners’ request in a used vehicle. The prototype includes accident and rollover detection, passenger detection, and GSM communication with the eCall PSAP service center. Two accident detection algorithms are implemented: the first relates to the collision detection based on exceeding the threshold of acceleration using an accelerometer to measure the dynamic force caused by movement and the gravity force, the second – the rollover detection based on exceeding the threshold of the vehicle angle of inclination using data coming from the accelerometer and a gyroscope. The prototype was tested in eight different test scenarios using laboratory accident simulators as well as during driving. The proposed solution of passenger detection is based on images coming from a built-in 360° view camera. In the authors’ approach the obtained images were transformed into linear ones and subject to subsequent operations – initial processing, object detection and redundant detection filtering. The authors utilize biometric methods in object detection such as Viola-Jones and Single Shot MultiBox Detector for face localization, and You Look Only Once for whole body detection. An additional testing mechanism for passenger detection are seat pressure pads that are independent of camera detection. All necessary sensors and communication components verified by practical experiments have been integrated on the custom designed motherboard with the NVIDIA Jetson Nano processing module installed. The tests confirmed the performance is sufficient for rapid accident detection, real-time image analysis and all mandatory eCall communication functions. Results of experiments were promising, providing high accident and passenger detection rates.

[1]  Selim Solmaz Switched stable control design methodology applied to vehicle rollover prevention based on switched suspension settings , 2011 .

[2]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[3]  Miroslaw Omieljanowicz,et al.  Accident Simulation for Extended eCall System Without Integration in Existing Car Onboard Systems , 2018, CISIM.

[4]  Mamun Bin Ibne Reaz,et al.  PREFERENCE AND PLACEMENT OF VEHICLE CRASH SENSORS , 2014 .

[5]  Suttipong Thajchayapong,et al.  Detection of Driving Events using Sensory Data on Smartphone , 2017, Int. J. Intell. Transp. Syst. Res..

[6]  Gangadhar,et al.  Reliable Automotive Crash Detection using Multi Sensor Decision Fusion , 2017 .

[7]  Elias Yaacoub,et al.  Safe driving: A mobile application for detecting traffic accidents , 2018, 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM).

[8]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[9]  Guochu Shou,et al.  Intelligent traffic accident detection system based on mobile edge computing , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[10]  Khalid Saeed,et al.  The Conceptual Approach of System for Automatic Vehicle Accident Detection and Searching for Life Signs of Casualties , 2018, ACSS.

[11]  Radu Ranta,et al.  Detection of human presence in a vehicle by vibration analysis , 2012 .

[12]  Ravi Kanth Reddy,et al.  S-CarCrash: Real-time crash detection analysis and emergency alert using smartphone , 2016, 2016 International Conference on Connected Vehicles and Expo (ICCVE).

[13]  Haimd M. Ali,et al.  Car Accident Detection and Notification System using Smartphone , 2017 .

[14]  M. Niezgoda,et al.  Collision detection algorithms in the ecall system , 2015 .

[15]  Saeid Fazli,et al.  A robust hybrid movement detection method in dynamic background , 2009, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[16]  Milan Koukol,et al.  Detection of persons in a vehicle using IR cameras and RFID technologies , 2015, 2015 Smart Cities Symposium Prague (SCSP).

[17]  Carsten Maple,et al.  Delay-Aware Accident Detection and Response System Using Fog Computing , 2019, IEEE Access.

[18]  Miroslaw Omieljanowicz,et al.  In-Car eCall Device for Automatic Accident Detection, Passengers Counting and Alarming , 2020, Trans. Comput. Sci..

[19]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[20]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Pietro Cerri,et al.  An embedded system for counting passengers in public transportation vehicles , 2014, 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[22]  Liang-Bi Chen,et al.  DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification , 2019, IEEE Access.

[23]  Xiaosong Li,et al.  Tiny YOLO Optimization Oriented Bus Passenger Object Detection , 2020 .

[24]  Khalid Saeed,et al.  A Speech-and-Speaker Identification System: Feature Extraction, Description, and Classification of Speech-Signal Image , 2007, IEEE Transactions on Industrial Electronics.

[25]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[26]  Miroslaw Omieljanowicz,et al.  Vehicle Passengers Detection for Onboard eCall-Compliant Devices , 2018, ACS.

[27]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).