Intelligent Smartphone based system for detecting speed bumps and reducing car speed

Daraghmi, Yousef‐Awwad; Daadoo, Motaz

doi:10.1051/matecconf/20167709006

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…For the detection of speed bumps and the lowering of vehicle speed, an intelligent system based on smartphone technology was devised in [22]. The device detects speed bumps with a gravity sensor, and the third equation of motion was used for speed reduction.…”

Section: Traditional-based Methodsmentioning

confidence: 99%

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

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The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.

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Section: Traditional-based Methodsmentioning

confidence: 99%

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Bala,

Adeshina,

Aibinu

2023

WEVJ

View full text Add to dashboard Cite

show abstract

“…In [33], the authors proposed a system to track the speed and location of a vehicle using mobile networks and GPS. Moreover, the authors in [34] proposed a system to monitor speed bumps and the speed of a vehicle. Given the large number of vehicles and the processing required, cloud computing combined with IoT is essential for the deployment of vehicle speed tracking [34,35].…”

Section: Speed Monitoring Systemsmentioning

confidence: 99%

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

Daraghmi

Helou²,

Daraghmi

et al. 2022

Future Internet

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The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

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Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview

Choong¹,

Nasir²,

Majeed³

et al. 2019

Lecture Notes in Mechanical Engineering

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Intelligent Smartphone based system for detecting speed bumps and reducing car speed

Cited by 11 publications

References 10 publications

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

Performance Evaluation of You Only Look Once v4 in Road Anomaly Detection and Visual Simultaneous Localisation and Mapping for Autonomous Vehicles

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview

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