[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120002-en":3,"doc-seo-120002-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120002,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Embedded Machine Learning-Based Road Conditions and Driving Behavior Monitoring","Road traffic crashes have increased in recent years, causing significant losses in human lives, property, and financial costs. To address this safety challenge, an embedded machine learning-based system is developed to monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The approach trains neural networks on a dataset covering normal street driving, speed bumps, circular yellow speed bumps, and aggressive maneuvers such as sudden start, sudden stop, and sudden entry. Evaluation shows 91.9% accuracy, 93.6% precision, and 92% recall, with inference time and memory usage suitable for resource-constrained embedded devices.","Embedded machine learning-based road conditions and driving  \nbehavior monitoring  \nBayan Mosleh1, Joud Hamdan1, Belal H. Sababha1, Yazan A. Alqudah2  \n1King Abdullah II School of Engineering, Princess Sumaya University for Technology, Amman, Jordan 2Electrical and Computer Engineering, University of West Florida, Florida, United States  \n\n| Article history:\u003Cbr>Received Jan 29, 2024 Revised Feb 28, 2024 Accepted Mar 5, 2024 | Car accident rates have increased in recent years, resulting in losses inhuman lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Driving behavior Edge machine learning Embedded systems Machine learning Road conditions |  |\n\nCorresponding Author:  \nBelal H. Sababha  \nComputer Engineering Department, King Abdullah II School of Engineering, Princess Sumaya University for Technology  \nAmman 11941, Jordan  \n[Email: b.sababha@psut.edu.jo](Email: b.sababha@psut.edu.jo)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe World Health Organization (WHO) reported in 2022 that approximately 1.3 million human lives are lost every year due to road traffic crashes. From 20 to 50 million others are injured, and among those are many who suffer from disabilities. This is in addition to significant economic losses [1] . These accidents are often caused by reckless driving, speeding, and unsafe road conditions. Artificial intelligence (AI) is becoming a primary contributor to advancements in different industries, including healthcare, education, technology, entertainment, military, and economics. It has made products and services more efficient and effective, enabling the analysis of large amounts of data quickly and accurately. As it advances, AI is expected to help protect and save human lives in many domains. One crucial domain is human safety on the roads. Many research has concentrated on utilizing embedded smartphone sensors and other methods in systems that are capable of predicting driving styles [2]–[6], driver behaviors [7]–[24], driving events [25],[26] and road conditions [27], [28] . Such systems are meant for safety or other applications that bring extra features and autonomy to vehicles [29]–[31] . The literature reports several works in machine learning for detecting and analyzing driving safety and road conditions.  \nThe research presented by Al-Refai et al. [32] proposed machine learning algorithms to classify different characteristics of a car environment and driving styles using in-car data collected from the vehicle's controller area network (CAN) and Ethernet. The data was collected, labeled, and used to grade road surface conditions if it is (soft, even, or there are holes in it), Traffic levels (light, moderate, heavy), and the style of driving if it is (normal or aggressiv","cbCaiiJieEBkP21W","https://ap.wps.com/l/cbCaiiJieEBkP21W","pdf",422443,1,12,"English","en",105,"# Article Info ABSTRACT\n# INTRODUCTION\n## AI for road safety and injury statistics\n## Prior work on driving style and road condition detection\n## Requirements of sensors and resource constraints","[{\"question\":\"What problem does the embedded system address?\",\"answer\":\"It targets the rising risk of road traffic accidents by monitoring road conditions and identifying unsafe or aggressive driving behaviors.\"},{\"question\":\"How does the system generate training data?\",\"answer\":\"It collects data for normal street and normal driving, different speed bump scenarios, and three aggressive actions: sudden start, sudden stop, and sudden entry.\"},{\"question\":\"What performance and deployment characteristics does the study report?\",\"answer\":\"The model achieves 91.9% accuracy, 93.6% precision, and 92% recall, and it runs with 34 ms inference time on an Arduino Nano 33 BLE Sense using about 2.6 kB peak RAM and 139.9 kB flash memory.\"}]","Embedded Machine Learning-Based Road Conditions and Driving Behavior Monitoring | 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