Search Results (132)

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

Human–Machine Interfaces (HMIs) in traditional automobiles are essential in connecting drivers, passengers, and vehicle systems. In automated vehicles, the HMI has become a critical component. A well-designed HMI facilitates effective human oversight, enhances situational awareness, and mitigates risks associated with system failures or unexpected scenarios. Simultaneously, it serves as a crucial safeguard against cyber threats, preventing unauthorized access and ensuring the integrity of vehicular operations in increasingly connected environments. This narrative review delves into the evolving landscape of automotive HMI design, emphasizing its role in enhancing user experience (UX) and safety. By exploring usability challenges, technological advancements, and the integration of rapidly evolving technologies such as AI (Artificial Intelligence), AR (Augmented Reality), and gesture-based controls, this study highlights how effective HMIs minimize cognitive load while maintaining functionality. Significant attention is given to the new challenges that arise from technological advancements in terms of security and safety. Full article

We developed a multi-feature drowsiness detection model employing eye aspect ratio (EAR), mouth aspect ratio (MAR), head pose angles (yaw, pitch, and roll), and a Raspberry Pi 4 for real-time applications. The model was trained on the NTHU-DDD dataset and optimized using long short-term memory (LSTM) deep learning algorithms implemented using TensorFlow version 2.14.0. The model enabled robust drowsiness detection at a rate of 10 frames per second (FPS). The system embedded with the model was constructed for live image capture. The camera placement was adjusted for optimal positioning in the system. Various features were determined under diverse conditions (day, night, and with and without glasses). After training, the model showed an accuracy of 95.23%, while the accuracy ranged from 91.81 to 95.82% in validation. In stationary and moving vehicles, the detection accuracy ranged between 51.85 and 85.71%. Single-feature configurations exhibited an accuracy of 51.85 to 72.22%, while in dual features, the accuracy ranged from 66.67 to 75%. An accuracy of 80.95 to 85.71% was attained with the integration of all features. Challenges in the drowsiness included diminished accuracy with MAR alone and delayed prediction during transitions from non-drowsy to drowsy status. These findings underscore the model’s applicability in detecting drowsiness while highlighting the necessity for refinement. Through algorithm optimization, dataset expansion, and the integration of additional features and feedback mechanisms, the model can be improved in terms of performance and reliability. Full article

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. Full article

This paper provides insights into where and how to integrate textile inductive electrodes into a car to record optimal-quality respiratory signals. Electrodes of various shapes and sizes were integrated into the seat belt and the seat back of a driving simulator car seat. The electrodes covered various parts of the body: upper back, middle back, lower back, chest, and waist. Three subjects completed driving circuits with their breathing signals being recorded. In general, signal quality while driving versus sitting still was similar, compared to a previous study of ours with no body movements. In terms of positioning, electrodes on seat belt provided better signal quality compared to seat back. Signal quality was directly proportional to electrode’s height on the back, with upper back outperforming both middle and lower back. Electrodes on the waist provided either similar or superior signal quality compared to electrodes on the chest. In terms of form factor, rectangular shape outperformed circular shape on seat back. Signal quality is proportional to the size of circular electrodes on seat back, and inversely proportional to size of rectangular electrode on seat belt. Full article

To address the need for an in-depth exploration of traffic accidents involving intelligent vehicles and to elucidate the influence mechanism of road environment interference factors on both assisted driving systems and human drivers during such accidents, a comprehensive analysis has been conducted using the System-Theoretic Process Analysis (STPA) framework. This analysis focuses on road static facilities, traffic dynamic characteristics, and instantaneous weather conditions in automobile traffic accidents that occur under the human-machine co-driving paradigm with integrated assisted driving functions. Based on these insights, an interference model tailored to road environment factors in traffic accidents of assisted driving vehicles has been constructed.Utilizing recent traffic accident cases in China, the Accident Map (AcciMap) methodology was employed to systematically classify and analyze all accident participants across six levels. Through this rigorous process, 59 accident factors were refined and optimized, culminating in a method for assessing the degree of interference posed by road environment factors in traffic accidents involving assisted driving vehicles. The ultimate objective of this research is to enhance the investigation of road environment interference factors following accidents that occur with diverse assisted driving functions in human-machine co-driving scenarios. By providing a structured and analytical approach, this study aims to support future research endeavors in developing effective traffic accident prevention countermeasures tailored to assisted driving vehicles. Full article

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications. Full article

Human-machine co-driving is an important stage in the development of automatic driving, and accurate recognition of driver behavior is the basis for realizing human-machine co-driving. However, traditional detection methods exhibit limitations in driver behavior detection, including low accuracy and slow processing efficiency. Aiming at these challenges, this paper proposes a driver behavior detection method that improves the Swin transformer model. First, the efficient channel attention (ECA) module is added after the self-attention mechanism of the Swin transformer model so that the channel features can be dynamically adjusted according to their importance, thus enhancing the model’s attention to the important channel features. Then, the image preprocessing of the public State Farm dataset and expansion of the original image dataset is carried out. Then, the parameters of the model are tuned. Finally, through the comparison test with other models, an ablation test is performed to verify the performance of the proposed model. The results show that the proposed model algorithm has a better performance in 10 classifications of driver behavior detection, with an accuracy of 99.42%, which is improved by 3.8% and 1.68% compared to Vgg16 and MobileNetV2, respectively. It can provide a theoretical reference for the development of an intelligent automobile human-machine co-driving system. Full article

Active Mobility (AM) currently presents an opportunity to change the paradigm of the competitive and dispersed city created by motorized mobility, revaluing the role of walking and cycling in generating more sustainable urban ecosystems. This article addresses the challenges and opportunities for AM to contribute to the regeneration of urban systems and the capacity for anticipation. This article analyzes AM using the Ecosystemic Urbanism (EU) as an analysis framework within its four axes: social cohesion, complexity, efficiency, and compactness and functionality. Through this analysis, the points of incidence of AM were identified within each of these axes. The study highlights the potential of AM to act as a transformative driver in urban development, integrating an ecological framework where urban systems are interconnected and mutually reinforced. This perspective reveals walking and cycling as a catalyst for reshaping urban interactions. In light of this, future cities must adopt a human urban scale through compactness that fosters complexity and diverse and engaging urban interactions. In addition, the enjoyability achieved through AM brings significant ecosystem benefits by promoting awareness of others, nature, and the interconnectedness between the individual and the city. This represents a new paradigm shift in which the automobile does not play the central role, allowing more sustainable ways of living together. Full article

The number of traffic accidents in a region rises as the vehicle–km value in traffic increases. Furthermore, since automobiles make up the highest proportion of vehicles in traffic, they represent the greatest weight in traffic accidents. This study aims to establish a model to predict the driver’s status (survived–injured–dead) as a result of the fatal-injury type of accident. The size of the vehicles suppresses the direct factors related to drivers by having a significant and dominant effect on the analysis of the results of the accidents by concealing the other important factors which must be taken into consideration with regard to the casualty levels of the drivers. Consequently, this paper focuses on automobiles, which are the most frequently involved vehicle type in accidents. Furthermore, the dataset representing the accidents that occurred in Turkey between 2015 and 2021 was employed for the analysis of the effects of the attributes of the drivers on the outcome of casualties for automobile-related accidents alone. The uniqueness of this research stems from being the first study in Turkey to investigate the severity levels of the drivers involved in automobile-related accidents. In addition, this study highlights the preventable factors investigated relatively less than other factors in the literature in order to establish a successful model. The difference between the success of the models with regard to accuracy obtained through dominant and investigated factors is only 5.0%. Random Forests, Naïve Bayes, and CHAID (Chi-squared Automatic Interaction Detection) models were established and compared as decision tree algorithms. The results revealed the fact that the CHAID model produced the most successful outcomes among them. Driver fault, gender, education level, and age, along with alcohol usage and surface condition, were found to be significant influential factors for the severity of traffic accidents. Full article

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. Full article

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles. Full article

Occupant comfort is a key consideration in automobile dynamics, with vibrations potentially causing long-term physical discomfort, especially for drivers. This study investigates the impact of adding TiO₂ and CuO nanoparticles to fuel on engine-induced vibrations. Experiments were conducted at various nanoparticle concentrations (0, 50, 100, and 150 ppm) and engine speeds (1000, 2000, and 3000 rpm). Key performance metrics, including kinematic viscosity, density, heating value, thermal conductivity, and brake power (BP), were analyzed. The results indicated that increasing nanoparticle concentration led to a rise in BP. The highest reduction in root mean square (RMS) vibration accelerations occurred at 3000 rpm and 150 ppm, with vibration reductions of 30.33% for CuO and 28.61% for TiO₂. Additionally, 8–10% of engine vibrations were transmitted to the steering wheel. The use of 150 ppm CuO nanoparticles resulted in reduced vibration transmission to the steering wheel at all tested speeds. These findings suggest that nanoparticle-enhanced fuels can significantly reduce engine vibrations, potentially improving driver comfort and reducing wear on vehicle components. Full article

Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as front and side cameras; these applications also include different configurations of sensors that provide information to the driver, such as objects approaching from different directions, such as from the front and sides. In this paper, we propose a practical and low-cost methodology to provide solutions using artificial intelligence techniques, as is the purpose of YOLO architecture, version 3, using hardware based on Nvidia’s Jetson TK1 architecture, and configurations in conventional cars. The results that we present demonstrate that these technologies can be applied in conventional cars, working with independent power to avoid causing problems in these cars, and we evaluate their application in the detection of people and cars in different situations, which allows information to be provided to the driver while performing maneuvers. The methodology that we provide can be replicated and scaled according to needs. Full article

Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall carbon emissions, there has been limited focus on individual provinces, particularly less developed provinces. Jiangxi Province, an underdeveloped province in southeastern China, recorded the highest GDP (Gross Domestic Product) growth rate in the country in 2022, and it holds significant potential for carbon emission reduction. This study uses data from Jiangxi Province’s 14th Five-Year Plan and Vision 2035 to create three carbon emission reduction scenarios and predict emissions. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology), along with various visualisation techniques, is employed to analyse the impacts of population size, primary electricity application level, GDP per capita, the share of the secondary industry in fixed-asset investment, and the number of civilian automobiles owned on carbon emissions. The study found that there is an inverted U-shaped curve relationship between GDP per capita and carbon emissions, car ownership is not a major driver of carbon emissions, and the development of primary electricity has significant potential as a means for reducing carbon emissions in Jiangxi Province. If strict environmental protection measures are implemented, Jiangxi Province can reach its peak carbon target by 2029, one year ahead of the national target. These findings provide valuable insights for policymakers in Jiangxi Province to ensure that their environmental objectives are met. Full article