DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS...DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS systems,particularly in highway scenarios,have reached or even surpassed human drivers in terms of precision and reliability[1].This mainstream development path is based on a replacement paradigm,whose central goal is to relieve human drivers of monotonous,repetitive tasks such as highway commuting,maximizing traffic efficiency and safety[2].This paradigm aims to replace error-prone human operators with a tireless,consistent machine intelligence.展开更多
文章基于某项目实车平台,根据ISO 26262《道路车辆功能安全》定义的功能安全要求,利用MAB(Micro Auto Box)工具对ADAS系统功能安全进行了研究。本研究用BOB设备对车辆进行了改装,在整车上搭建了MAB设备,并编写了功能安全脚本,在MAB中运...文章基于某项目实车平台,根据ISO 26262《道路车辆功能安全》定义的功能安全要求,利用MAB(Micro Auto Box)工具对ADAS系统功能安全进行了研究。本研究用BOB设备对车辆进行了改装,在整车上搭建了MAB设备,并编写了功能安全脚本,在MAB中运行脚本,对ADAS子系统自适应巡航系统ACC的EBCM控制器进行故障注入研究。结果表明,功能安全脚本可以在MAB中成功运行,可以成功利用MAB设备对EBCM模块进行故障注入。最初车辆减速度控制策略不满足ACC系统的功能安全需求,车辆存在高速制动力过大的问题。经过多次联调和测试,最终车辆的制动减速度控制在要求范围内,满足了ACC系统的功能安全需求,保障了车内驾乘人员的安全,提高了驾乘舒适性。展开更多
Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
基金supported in part by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0145/2023/RIA3)in part by the DeSciCPI Project from the Obuda University,Hungary.
文摘DRIVEN by advancements in artificial intelligence technologies such as deep learning,core intelligent driving technologies like advanced driver assistance systems(ADAS)have made significant advances.Some advanced ADAS systems,particularly in highway scenarios,have reached or even surpassed human drivers in terms of precision and reliability[1].This mainstream development path is based on a replacement paradigm,whose central goal is to relieve human drivers of monotonous,repetitive tasks such as highway commuting,maximizing traffic efficiency and safety[2].This paradigm aims to replace error-prone human operators with a tireless,consistent machine intelligence.
文摘文章基于某项目实车平台,根据ISO 26262《道路车辆功能安全》定义的功能安全要求,利用MAB(Micro Auto Box)工具对ADAS系统功能安全进行了研究。本研究用BOB设备对车辆进行了改装,在整车上搭建了MAB设备,并编写了功能安全脚本,在MAB中运行脚本,对ADAS子系统自适应巡航系统ACC的EBCM控制器进行故障注入研究。结果表明,功能安全脚本可以在MAB中成功运行,可以成功利用MAB设备对EBCM模块进行故障注入。最初车辆减速度控制策略不满足ACC系统的功能安全需求,车辆存在高速制动力过大的问题。经过多次联调和测试,最终车辆的制动减速度控制在要求范围内,满足了ACC系统的功能安全需求,保障了车内驾乘人员的安全,提高了驾乘舒适性。
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
