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"Automobiles Design and construction Data processing."
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Car Crashes without Cars
Every workday we wrestle with cumbersome and unintuitive technologies. Our response is usually \"That's just the way it is.\" Even technology designers and workplace managers believe that certain technological changes are inevitable and that they will bring specific, unavoidable organizational changes. In this book, Paul Leonardi offers a new conceptual framework for understanding why technologies and organizations change as they do and why people think those changes had to occur as they did. He argues that technologies and the organizations in which they are developed and used are not separate entities; rather, they are made up of the same building blocks: social agency and material agency. Over time, social agency and material agency become imbricated--gradually interlocked--in ways that produce some changes we call \"technological\" and others we call \"organizational.\" Drawing on a detailed field study of engineers at a U.S. auto company, Leonardi shows that as the engineers developed and used a a new computer-based simulation technology for automotive design, they chose to change how their work was organized, which then brought new changes to the technology.Each imbrication of the social and the material obscured the actors' previous choices, making the resulting technological and organizational structures appear as if they were inevitable. Leonardi suggests that treating organizing as a process of sociomaterial imbrication allows us to recognize and act on the flexibility of information technologies and to create more effective work organizations.
eBook
Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm
Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.
Journal Article
Computer Engineering Applications in Electronic, Biomedical, and Automotive Systems
Computer Engineering covers a broad range of applications and overlaps with other fields, including materials science, electrical/electronics, vehicles, physics, and statistics. This book provides insights into a few computer engineering applications in electronics, and in the medical and the automotive fields where advances in robotics, embedded systems, and sensors are creating a bold future. The chapters are written from the perspectives of scientists and engineering consultants who have broad experiences across multiple vendors and manufacturers of systems. Their unique perspectives are formed from work on consumer products including research, development, safety, legal compliance, and standards for products.
eBook
ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation
We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.
Journal Article
Study on a Novel Variable Valve Timing and Lift Mechanism for a Miller Cycle Diesel Engine
Thermal efficiency and power density improvement are the main research foci of the literature on diesel engines. The Miller cycle is considered to be one of the most promising methods of diesel engine operation. In this study, a fully variable valve timing and lift mechanism (CD-HFVVS) was studied to determine the possibility of a Miller operation. Firstly, the valve seat impact buffer in the mechanism was tested, which proved that the buffer can effectively eliminate the valve seat impact. Then the influences of different speeds and oil temperatures were studied. The results show that the valve opening duration is prolonged when engine speed increases, and the valve lift and duration are reduced while the oil temperature increased. The valve timing and lift can be fully adjusted by changing the oil discharge position and the initial plunger position, which further proves that CD-HFVVS can achieve the performance optimization of the Miller cycle. By using the mechanism, a single cylinder test was performed. By using variable inlet valve timing, the fuel efficiency can be effectively improved and the peak pressure and in-cylinder average temperature can both be suppressed.
Journal Article
mm-DSF: A Method for Identifying Dangerous Driving Behaviors Based on the Lateral Fusion of Micro-Doppler Features Combined
To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver’s driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method.
Journal Article
State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM
Battery state of health (SOH) estimation is a prerequisite for battery health management and is vital for second-life utilization. Existing techniques implemented in well-controlled experimental conditions fail to reflect complex working conditions during actual vehicular operation. In this article, a novel SOH estimation method for battery systems in real-world electric vehicles (EVs) is presented by combing results of regional capacity calculation and a light gradient boosting machine (LGBM) model. The LGBM model is used to capture the relationship between battery degeneration and influential factors based on datasets from real-world EVs. The regional capacity, which is calculated through incremental capacity analysis with a Gaussian smoothing filter, is utilized to reflect the battery degradation level while ensuring high flexibility and applicability. Accumulated mileage, average charging current, average charging temperature, and start and end of SOC values are chosen as influential factors for model establishment. The effectiveness, complexity, superiority, and robustness of the proposed method are verified using data from real-world EVs. Results indicate accurate SOH estimation can be achieved with an average absolute error of only 0.89 Ah, where the MAPE and RMSE of the test vehicles are 2.049% and 1.153%, respectively.
Journal Article
Development of a Linking System Between Vehicle’s Computer and Alexa Auto
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.
Journal Article
The Safety of Intelligent Driver Support Systems
Road telematics and driver assistance systems offer a real opportunity to aid mobility and road safety. However, they also raise numerous questions. Problems related to the design and evaluation of intelligent driver support systems (IDSSs) and social perspectives related to their large scale introduction may only be fully addressed from a multi-disciplinary viewpoint. People from both engineering and social sciences, should be involved and this book provides such knowledge from both a human and social factors perspective.
eBook