Vision-Based Road Rage Detection Framework in Automotive Safety Applications

Deciphering NF-kappaB pathways in smoking-related lung carcinogenesis

One of the main causes of death worldwide is lung cancer, which is largely caused by cigarette smoking. The crucial transcription factor NF-κB, which controls inflammatory responses and various cellular processes, is a constitutively present cytoplasmic protein strictly regulated by inhibitors like IκB proteins. Upon activation by external stimuli, it undergoes phosphorylation, translocates into the nucleus, and modulates the expression of specific genes. The incontrovertible association between pulmonary malignancy and tobacco consumption underscores and highlights a public health concern. Polycyclic aromatic hydrocarbons and nitrosamines, potent carcinogenic compounds present in the aerosol emitted from combusted tobacco, elicit profound deleterious effects upon inhalation, resulting in severe perturbation of pulmonary tissue integrity. The pathogenesis of smoking-induced lung cancer encompasses an intricate process wherein NF-κB activation plays a pivotal role, triggered by exposure to cigarette smoke through diverse signaling pathways, including those associated with oxidative stress and pro-inflammatory cytokines. Unraveling the participation of NF-κB in smoking-induced lung cancer provides pivotal insights into molecular processes, wherein intricate crosstalk between NF-κB and pathways such as MAPK and PI3K-Akt amplifies the inflammatory response, fostering an environment conducive to the formation of lung cancer. This study reviews the critical function of NF-κB in the complex molecular pathways linked to the initiation and advancement of lung carcinogenesis as well as potential treatment targets. See also the graphical abstract(Fig. 1).

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach

Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions for proper driving and estimate drivers' capability of stable driving behavior and road safety. Many studies have proved that the driver's emotions are the significant factors that manage the driver's behavior, leading to severe vehicle collisions. Therefore, continuous monitoring of drivers' emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver's emotions in different poses, occlusions, and illumination conditions to achieve this goal. To determine the emotions, a fusion of Gabor and LBP features has been utilized to find the features and been classified using a support vector machine classifier combined with a convolutional neural network. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively.

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods.

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

BACKGROUND

The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution.

METHODS

This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram-EEG, electrocardiogram-ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine.

RESULTS

The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance.

CONCLUSION

The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient's life, with the support of the e-Call system.