Journal articles: 'COMPUTER ASSISTED DRIVING'

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Nicolescu, Florin Adrian, Georgia Cezara Avram, Andrei Mario Ivan, and Adrian Theodor Mantea. "Computer Assisted Selection of Servomotor Driving System for Linear Motion NC Axis Experimental Stand." Applied Mechanics and Materials 762 (May 2015): 61–66. http://dx.doi.org/10.4028/www.scientific.net/amm.762.61.

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The paper presents the works performed by the authors in the field of structural and functional optimization numerically controlled (NC) axes. The study includes two computing applications developed by second author of the paper in a PhD thesis related on NC axes’ structural and functional optimization. The first computing application is used for calculating the total reflected inertia of a linear motion NC axis (total inertial loads' reducing on the driving motor's shaft level). The second computing application is used for both preliminary selection of the driving servomotor (by checking first the accomplishment of the kinematic criterion) and a secondary selection of the electric motor (by checking in a second stage the accomplishment of the static and dynamic criterion). By mean of both software applications optimal matching of servomotor driving system with available NC axis mechanical structure may be determined. The analyzed linear motion NC axis is part of an experimental stand (existing in the MMS department from EMTS faculty), that supplementary to the driving servomotor, includes a belt drive transmission, a ball screw - bearings assembly and a driven element guided by ball rail system.

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Nicolescu, Florin Adrian, Georgia Cezara Avram, Andrei Mario Ivan, and Adrian Theodor Mantea. "Brushless Servomotor’s Thermal Behavior Computer Assisted Evaluation for a Linear Motion NC Axis Experimental Stand." Applied Mechanics and Materials 762 (May 2015): 21–26. http://dx.doi.org/10.4028/www.scientific.net/amm.762.21.

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The paper presents works carried out by the authors in the field of NC axes’ structural and functional optimization. This paper includes the results obtained by using a MathCAD application (developed in the doctoral thesis of the second author) for the servomotor's thermal behavior computer assisted evaluation. The analyzed servomotor is included in the driving system of a linear motion NC axis experimental testing stand, (existing in MMS department from EMTS Faculty). The NC axis of the experimental stand integrates a FAGOR brushless servomotor, a 1:1 ratio belt drive intermediary transmission, a ball screw - bearings assembly (lead screw - ball nut - bearings) and a driven element guided through a ball rail system. The MathCAD application was developed in order to perform specific calculation for servomotor's thermal energy dissipation and maximum operating temperature evaluation, and allows to perform the assisted final check and optimum selection of the electrical driving servomotors based on these results. The paper presents, through some screenshots from running the MathCAD application, the computer assisted evaluation procedure and results of the brushless servomotor's thermal behavior analysis corresponding to analyzed NC axis (included in the experimental stand).

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Yan, Haibo. "Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment." Computational Intelligence and Neuroscience 2022 (June 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/4390394.

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In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters.

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Ehlgen, Tobias, Tomas Pajdla, and Dieter Ammon. "Eliminating Blind Spots for Assisted Driving." IEEE Transactions on Intelligent Transportation Systems 9, no. 4 (2008): 657–65. http://dx.doi.org/10.1109/tits.2008.2006815.

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Intelligence and Neuroscience, Computational. "Retracted: Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment." Computational Intelligence and Neuroscience 2023 (December 13, 2023): 1. http://dx.doi.org/10.1155/2023/9759151.

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Cui, Jianwei, Yucheng Shang, Siji Yu, and Yuanbo Wang. "Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology." Actuators 13, no. 6 (2024): 230. http://dx.doi.org/10.3390/act13060230.

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The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

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Huang, Xueda, Shaowen Wang, Guanqiu Qi, et al. "Driver Distraction Detection Based on Cloud Computing Architecture and Lightweight Neural Network." Mathematics 11, no. 23 (2023): 4862. http://dx.doi.org/10.3390/math11234862.

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Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved.

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Bieri, Rahel, Michael Jäger, Nicole Gruber, Tobias Nef, René M. Müri, and Urs P. Mosimann. "A novel computer test to assess driving-relevant cognitive functions – a pilot study." International Psychogeriatrics 26, no. 2 (2013): 229–38. http://dx.doi.org/10.1017/s104161021300183x.

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ABSTRACTBackground:The assessment of driving-relevant cognitive functions in older drivers is a difficult challenge as there is no clear-cut dividing line between normal cognition and impaired cognition and not all cognitive functions are equally important for driving.Methods:To support decision makers, the Bern Cognitive Screening Test (BCST) for older drivers was designed. It is a computer-assisted test battery assessing visuo-spatial attention, executive functions, eye–hand coordination, distance judgment, and speed regulation. Here we compare the performance in BCST with the performance in paper and pencil cognitive screening tests and the performance in the driving simulator testing of 41 safe drivers (without crash history) and 14 unsafe drivers (with crash history).Results:Safe drivers performed better than unsafe drivers in BCST (Mann–Whitney U test: U = 125.5; p = 0.001) and in the driving simulator (Student's t-test: t(44) = –2.64, p = 0.006). No clear group differences were found in paper and pencil screening tests (p > 0.05; ns). BCST was best at identifying older unsafe drivers (sensitivity 86%; specificity 61%) and was also better tolerated than the driving simulator test with fewer dropouts.Conclusions:BCST is more accurate than paper and pencil screening tests, and better tolerated than driving simulator testing when assessing driving-relevant cognition in older drivers.

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Geng, Wei Feng. "Design and Implementation of SMS-Based Business English Education System." Advanced Materials Research 971-973 (June 2014): 2564–67. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.2564.

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With increasing popularity and development of computer and network technology, the driving force behind business English teaching reform and providing development. Internet assisted teaching of English in many ways irreplaceable advantages, it is with traditional teaching methods are complementary and mutually balanced, played a traditional teaching does not have the function and role in business English teaching. Based on the features and role of the Internet to support teaching business English courses, explore network-assisted college English teaching model professional business English.

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Hammond, Philip. "From computer-assisted to data-driven: Journalism and Big Data." Journalism 18, no. 4 (2016): 408–24. http://dx.doi.org/10.1177/1464884915620205.

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Despite claims of continuity, contemporary data journalism is quite different from the earlier tradition of computer-assisted reporting. Although it echoes earlier claims about being scientific and democratic, these qualities are understood as resulting from better data access rather than as being something achieved by the journalist. In the context of Big Data in particular, human subjectivity tends to be downgraded in importance, even understood as getting in the way if it means hubristically theorising about causation rather than working with correlation and allowing the data to speak. Increasing ‘datafication’ is not what is driving changes in the profession, however. Rather, the impact of Big Data tends to be understood in ways that are consonant with pre-existing expectations, which are shaped by the broader contemporary post-humanist political context. The same is true in academic analysis, where actor–network theory seems to be emerging as the dominant paradigm for understanding data journalism, but in largely uncritical ways.

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Wang, Meng. "Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings." Transportation Research Part C: Emerging Technologies 91 (June 2018): 276–95. http://dx.doi.org/10.1016/j.trc.2018.04.010.

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Yang, Lei, Lingling Zhang, Zongjian He, Jiannong Cao, and Weigang Wu. "Efficient Hybrid Data Dissemination for Edge-Assisted Automated Driving." IEEE Internet of Things Journal 7, no. 1 (2020): 148–59. http://dx.doi.org/10.1109/jiot.2019.2946276.

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Li, Mushu, Jie Gao, Lian Zhao, and Xuemin Shen. "Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving." IEEE Transactions on Vehicular Technology 70, no. 6 (2021): 5318–31. http://dx.doi.org/10.1109/tvt.2021.3062653.

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Narkhede, Manish, Atharva Potdar, Isha Kusalkar, Pooja Sonawane, and Manali Galande. "ADVANCED DRIVING ASSISTANCE WITH TRACKING AND ACCIDENT AVOIDANCE SYSTEM." International Journal of Engineering Applied Sciences and Technology 7, no. 9 (2023): 177–84. http://dx.doi.org/10.33564/ijeast.2023.v07i09.027.

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Assisted vehicles are the smart vehicle that detects the obstacles, prevents any major accidents by tackling obstacles, and alerts nearby hospitals in case of an accident. This research introduces a low-cost prototype of a miniature assisted vehicle model, utilizing readily accessible and straightforward technologies, which has the capability to be a hallmark of the forthcoming technological revolution. Motive is to avoid accidents due to frequent mistakes made by the driver. This prototype leverages the Jetson Nano controller and H-bridge drives for controlling the two DC motors, resulting in a highly functional vehicle. The implementation of cutting-edge technologies such as GPS for location determination, ultrasonic sensors for obstacle detection and avoidance, computer vision for pedestrian detection and image processing for intelligent systems, enhances its capabilities. In the event of an accident, the accelerometer gets activated and an alert message is rapidly sent with the live location to the registered mobile number and nearby hospital via GPS and GSM. These vehicles are seen as a solution to urban transportation challenges, offering safe and timely transportation, and could have potential applications in military scenarios.

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Su, Shujing, Rui Sun, and Kunran Zhang. "Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving." Theoretical and Natural Science 51, no. 1 (2024): 128–36. http://dx.doi.org/10.54254/2753-8818/51/2024ch0188.

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Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.

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Singh, Baljeet, Nitin Kumar, Irshad Ahmed, and Karun Yadav. "Real-Time Object Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3159–60. http://dx.doi.org/10.22214/ijraset.2022.42820.

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Abstract: The computer vision field known as real-time acquisition is large, dynamic, and complex. Local image process refers to the acquisition of one object in an image, while Objects refers to the acquisition of multiple objects in an image. In digital photos and videos, this sees semantic class objects. Tracking features, video surveilance, pedestrian detection, census, self-driving cars, face recognition, sports tracking, and many other applications used to find real-time object. Convolution Neural Networks is an in-depth study tool for OpenCV (Opensource Computer Vision), a set of basic computer-assisted programming tasks. Computer visualization, in-depth study, and convolutional neural networks are some of the words used in this paper..

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Wang, Buyun, Yi Liang, Dezhang Xu, Zhihong Wang, and Jing Ji. "Design on electrohydraulic servo driving system with walking assisting control for lower limb exoskeleton robot." International Journal of Advanced Robotic Systems 18, no. 1 (2021): 172988142199228. http://dx.doi.org/10.1177/1729881421992286.

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According to the characteristics of human gait and the requirements of power assistance, locomotive mechanisms and electrohydraulic servo driving are designed on a lower limb exoskeleton robot, in which the miniaturization and lightweight of driving system are realized. The kinematics of the robot is analyzed and verified via the typical movements of the exoskeleton. In this article, the simulation on the power of joints during level walking was analyzed in ADAMS 2016, which is a multibody simulation and motion analysis software. Motion ranges and driving strokes are then optimized. A proportional integral derivative (PID) control method with error estimation and pressure compensation is proposed to satisfy the requirements of joints power assistance and comply with the motion of human lower limb. The proposed method is implemented into the exoskeleton for assisted walking and is verified by experimental results. Finally, experiments show that the tracking accuracy and power-assisted performance of exoskeleton robot joints are improved.

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Yan, Xingda, Craig Allison, James Fleming, Neville Stanton, and Roberto Lot. "The Benefit of Assisted and Unassisted Eco-Driving for Electrified Powertrains." IEEE Transactions on Human-Machine Systems 51, no. 4 (2021): 403–7. http://dx.doi.org/10.1109/thms.2021.3086057.

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Liu, Luning, Zhaoming Lu, Luhan Wang, Xin Chen, and Xiangming Wen. "Large-volume data dissemination for cellular-assisted automated driving with edge intelligence." Journal of Network and Computer Applications 155 (April 2020): 102535. http://dx.doi.org/10.1016/j.jnca.2020.102535.

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Liu, Haoyu. "Research Progress on 2D Human Pose Estimation Based on Deep Learning." Theoretical and Natural Science 51, no. 1 (2024): 19–25. http://dx.doi.org/10.54254/2753-8818/51/2024ch0130.

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Human pose estimation (HPE) is a significant area of study within computer vision and artificial intelligence, which locates the position and pose of human joints through images or videos. HPE has advanced progress in computer vision and deep learning fields at the theoretical level, improved the efficiency and accuracy of neural networks, and supported the development of natural and efficient human-computer interaction systems. At the application level, HPE has extensive value in many fields, including security monitoring, motion analysis and health management, VR&AR, robotics technology, film and television production and animation, intelligent driving and assisted driving. This paper divides 2D HPE tasks into three categories: two-stage (top-down and bottom-up), single-stage, and other improvement strategies (Transformer-based methods and post-processing optimization methods). Although deep learning-based pose estimation algorithms have achieved remarkable results, they still face challenges, such as prediction in complex scenes, crowd and joint occlusion problems, and lightweight design of models. Future work should pay more attention to these challenges and extend pose estimation technology from 2D to 3D contexts.

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Liu, Haoyu. "Research Progress on 2D Human Pose Estimation Based on Deep Learning." Theoretical and Natural Science 41, no. 1 (2024): 43–49. http://dx.doi.org/10.54254/2753-8818/41/2024ch0130.

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Human pose estimation (HPE) is a significant area of study within computer vision and artificial intelligence, which locates the position and pose of human joints through images or videos. HPE has advanced progress in computer vision and deep learning fields at the theoretical level, improved the efficiency and accuracy of neural networks, and supported the development of natural and efficient human-computer interaction systems. At the application level, HPE has extensive value in many fields, including security monitoring, motion analysis and health management, VR&AR, robotics technology, film and television production and animation, intelligent driving and assisted driving. This paper divides 2D HPE tasks into three categories: two-stage (top-down and bottom-up), single-stage, and other improvement strategies (Transformer-based methods and post-processing optimization methods). Although deep learning-based pose estimation algorithms have achieved remarkable results, they still face challenges, such as prediction in complex scenes, crowd and joint occlusion problems, and lightweight design of models. Future work should pay more attention to these challenges and extend pose estimation technology from 2D to 3D contexts.

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Rodrigues, Rita, Ana Bastos Silva, Luís Vasconcelos, and Álvaro Seco. "The Longitudinal Driving Behavior of a Vehicle Assisted with Lv2 Driving Automation: An Empirical Study." Journal of Advanced Transportation 2022 (November 23, 2022): 1–17. http://dx.doi.org/10.1155/2022/3073393.

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As the number of automated vehicles in our transportation system increases, it becomes increasingly important to understand how automation affects their driving behavior. This study defines and tests a methodology based on optimization methods to incorporate the longitudinal driving behavior of automated vehicles in the Wiedemann 99 car-following model. A pilot study was recently conducted in Portugal using a Mercedes-Benz of 2017 assisted with level 2 driving automation to gather empirical data. In total, 61 car-following events were used to support the calibration and validation tasks. The calibration error sustains the methodology’s descriptive capability to simulate the driving behavior of AVs, and the validation error sustains that the calibrated model parameters can reproduce the dynamic driving behavior of AVs with reasonable consistency and robustness. A total of seven model parameters were estimated and are in line with the trends often described in the literature on automated vehicles but also highlight differences that can be explained by different development and deployment strategies. Nevertheless, since empirical data from automated vehicles are hard to get, the presented work findings are also valuable for improving and validating future modeling efforts.

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Rudigier, Martin, Georg Nestlinger, Kailin Tong, and Selim Solmaz. "Development and Verification of Infrastructure-Assisted Automated Driving Functions." Electronics 10, no. 17 (2021): 2161. http://dx.doi.org/10.3390/electronics10172161.

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Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.

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Gruden, Timotej, and Grega Jakus. "Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving." Applied Sciences 13, no. 11 (2023): 6649. http://dx.doi.org/10.3390/app13116649.

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In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.

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Crowe, James W., and William J. Bailey. "Self-Interest and Attitudes about Legislation Controlling Alcohol." Psychological Reports 76, no. 3 (1995): 995–1003. http://dx.doi.org/10.2466/pr0.1995.76.3.995.

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A stratified random sample of 505 adult Indiana residents living in households with telephones was surveyed using random-digit dialing to assess their attitudes about nine specific legislative proposals to control drunk driving or underage drinking and to assess the effects of self-interest on those attitudes. A two-stage Mitofsky-Waksberg design was used in the computer-assisted telephone-interview process. There was widespread support for all nine proposals, but self-interest, related to personal vulnerability for enforcement of those measures, reduced the support of regular drinkers for drunk-driving controls compared with nondrinkers' support. Parents of children aged 18 and younger were less likely than nonparents to support imposing parental liability for the consequences of children's underage drinking.

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Dai, Huatong, Pengzhan Chen, and Hui Yang. "Driving Torque Distribution Strategy of Skid-Steering Vehicles with Knowledge-Assisted Reinforcement Learning." Applied Sciences 12, no. 10 (2022): 5171. http://dx.doi.org/10.3390/app12105171.

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Due to the advantages of their drive configuration form, skid-steering vehicles with independent wheel drive systems are widely used in various special applications. However, obtaining a reasonable distribution of the driving torques for the coordinated control of independent driving wheels is a challenging problem. In this paper, we propose a torque distribution strategy based on the Knowledge-Assisted Deep Deterministic Policy Gradient (KA-DDPG) algorithm, in order to minimize the desired value tracking error as well as achieve the longitudinal speed and yaw rate tracking control of skid-steering vehicles. The KA-DDPG algorithm combines knowledge-assisted learning methods with the DDPG algorithm, within the framework of knowledge-assisted reinforcement learning. To accelerate the learning process of KA-DDPG, two assisted learning methods are proposed: a criteria action method and a guiding reward method. The simulation results obtained, considering different scenarios, demonstrate that the KA-DDPG-based torque distribution strategy allows a skid-steering vehicle to achieve high performance, in tracking the desired value. In addition, further simulation results, also, demonstrate the contributions of knowledge-assisted learning methods to the training process of KA-DDPG: the criteria action method speeds up the learning speed by reducing the agent’s random action selection, while the guiding reward method achieves the same result by sharpening the reward function.

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Huang, Weiqiang, Kai Lu, and Weiqiang Huang. "Design of Permanent Magnet Synchronous Motor Power-assisted System Based on Field Oriented Control Algorithm." Journal of Physics: Conference Series 2246, no. 1 (2022): 012065. http://dx.doi.org/10.1088/1742-6596/2246/1/012065.

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Abstract Aiming at the integrated surgical robot, a set of power-assisted control system is designed to overcome the transmission resistance between master and slave hands, so that the operator feels light and comfortable during operation. The system includes permanent magnet synchronous motor, driver, controller, torque sensor, upper computer. The field oriented control algorithm is discussed and is used to control the permanent magnet synchronous motor. STM32 is used as the main control chip, DRV8301 is used as the driving chip, and the upper computer software, which has human-computer interaction interface and real-time torque signal display interface, is written by C#. A non-contact torque sensor and corresponding signal processing module is designed. The experimental results show that with the operator’s start-stop and reversing action, the q-axis current of the motor changes rapidly, and the amplitude of steady-state changes is small. This means that the designed system has the advantages of stable assist torque and fast response.

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Bumbu, Neculai-Eduard, and Mihaita Horodinca. "AN APPROACH ON THE DESCRIPTION OF A FLAT DRIVING BELT BEHAVIOUR MIRRORED IN TRANSMITTED MECHANICAL POWER." International Journal of Modern Manufacturing Technologies 15, no. 2 (2023): 7–19. http://dx.doi.org/10.54684/ijmmt.2023.15.2.7.

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For driving belt condition monitoring, the main interest is the certification of the capacity to keep their qualities unchanged over a long period of time and secondary to detect the imminence of the catastrophic failure. This paper presents a study on the behaviour detection and description of a flat driving belt health condition, used in a rotary machine electrically driven, particularly a lathe headstock gearbox running idle. It was discovered that in the mechanical power transmitted from electromotor to gearbox via a flat belt some specific sinusoidal components (a fundamental and some harmonics) of variable power are generated. The description of these power components (by values of amplitude, frequency and phase at origin of time) is indirectly detectable in the evolution of the active electrical power absorbed by the drive electromotor. Two arguments are available for this approach. Firstly, there is a reasonable assumption that between the mechanical power and the active electrical power there is an approximated proportionality relationship through the power efficiency. Secondly, the evolution of the active electrical power (or mechanical power as well) is a deterministic signal with a low level of noise. A simple computer assisted procedure of active electrical power signal acquisition and data processing was conceived, the detection was done by computer aided curve-fitting procedures in Matlab applied on active electrical power evolution absorbed by the driving motor in stationary working regimes (the electromotor playing the role of a mechanical power sensor). Mainly two ways of graphic representation have been proposed in order to describe the variable power generated by this flat belt (in time and frequency domains). The behaviour of many other types of belts involved in rotary machines driving can be similarly described.

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Zhao, Shuanfeng, Guodong Han, Qingqing Zhao, and Pei Wei. "Prediction of Driver’s Attention Points Based on Attention Model." Applied Sciences 10, no. 3 (2020): 1083. http://dx.doi.org/10.3390/app10031083.

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The current intelligent driving system does not consider the selective attention mechanism of drivers, and it cannot completely replace the drivers to extract effective road information. A Driver Visual Attention Network (DVAN), which is based on deep learning attention model, is proposed in our paper, in order to solve this problem. The DVAN is aimed at extracting the key information affecting the driver’s operation by predicting the driver’s attention points. It completes the fast localization and extraction of road information that is most interesting to drivers by merging local apparent features and contextual visual information. Meanwhile, a Cross Convolutional Neural Network (C-CNN) is proposed in order to ensure the integrity of the extracted information. Here, we verify the network on the KITTI dataset, which is the largest computer vision algorithm evaluation data set in the world’s largest autonomous driving scenario. Our results show that the DVAN can quickly locate and identify the target that the driver is most interested in a picture, and the average accuracy of prediction is 96.3%. This will provide useful theoretical basis and technical methods that are related to visual perception for intelligent driving vehicles, driving training and assisted driving systems in the future.

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Kim, Seung-Wook, Keunsoo Ko, Haneul Ko, and Victor C. M. Leung. "Edge-Network-Assisted Real-Time Object Detection Framework for Autonomous Driving." IEEE Network 35, no. 1 (2021): 177–83. http://dx.doi.org/10.1109/mnet.011.2000248.

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Choi, Yukyung, Namil Kim, Soonmin Hwang, et al. "KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving." IEEE Transactions on Intelligent Transportation Systems 19, no. 3 (2018): 934–48. http://dx.doi.org/10.1109/tits.2018.2791533.

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Nusem, Erez, Judy Matthews, and Cara Wrigley. "Toward Design Orientation and Integration: Driving Design from Awareness to Action." Design Issues 35, no. 3 (2019): 35–49. http://dx.doi.org/10.1162/desi_a_00548.

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Design is increasingly recognized as offering strategic value to business in driving innovation, and has assisted organizations in balancing the needs of several stakeholders while maintaining competitiveness. However, for organizations which are not design-oriented, realizing outcomes through design can present a complex challenge. This paper presents the findings of a two-year action research project, and contributes a method for building an appetite for design, developing an awareness of the value design can offer, and for taking action within organizational constraints. Outcomes include enhanced perception of design methods, a clearer understanding of stakeholders, and the contribution of a framework for integrating design in organizations yet unfamiliar with the value of design.

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Jung, Soyi, Joongheon Kim, Marco Levorato, Carlos Cordeiro, and Jae-Hyun Kim. "Infrastructure-Assisted On-Driving Experience Sharing for Millimeter-Wave Connected Vehicles." IEEE Transactions on Vehicular Technology 70, no. 8 (2021): 7307–21. http://dx.doi.org/10.1109/tvt.2021.3094806.

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WIECZOREK, WILLIAM F., AMY L. MIRAND, and CHRISTOPHER P. CALLAHAN. "Perception of the Risk of Arrest for Drinking and Driving." Criminal Justice and Behavior 21, no. 3 (1994): 312–24. http://dx.doi.org/10.1177/0093854894021003002.

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This study examined the relationships among drinking measures, drinking and driving variables, and the perception of the risk of being arrested for driving while impaired by alcohol (DWI). Random-digit dialing procedures were used in association with computer-assisted telephone interviewing to collect data from 453 drivers aged 18 and older who drank alcoholic beverages in the last 12 months. Subjects were categorized by their perceptions of the risk for DWI arrest into a high probability of arrest group (at least 1 in 10 chance for arrest) and a low probability group (a 1 in 100 chance or less). Men in the high probability of arrest group were significantly heavier drinkers than those in the low probability group. Women who drank heavily in a single sitting also tended to express a higher perceived risk of DWI arrest. Age differences in DWI arrest probability also were uncovered. These results suggest that DWI prevention efforts need to be directed at specific groups and that for some groups, such as heavier drinkers, efforts other than deterrence are needed.

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Rundo, Francesco. "Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario." Computation 9, no. 11 (2021): 117. http://dx.doi.org/10.3390/computation9110117.

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The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.

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Shi-Ming Huang, Shi-Ming Huang, and Yung-Yao Liu Shi-Ming Huang. "Machine Learning Improves Environmental Sustainability: A Teaching Case of Prediction about Scrapping Inventory." International Journal of Computer Auditing 5, no. 1 (2023): 004–31. http://dx.doi.org/10.53106/256299802023120501002.

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<p>As technology evolves and society changes, accounting and auditing students must develop diverse skills, including technical proficiency and critical thinking. Environmental, Social, and Governance (ESG) concerns are increasingly important, driving nations and corporations to take action. Investors prioritize ESG criteria, while consumers prefer eco-friendly products and services. Preventing Greenwashing is important for future auditors. This teaching case high lights the role of accountants in managing E-waste under the Environmental pillar of ESG. Students explore new technologies like Computer Assisted Audit Techniques (CAATs) through hands-on exercises to address these challenges.</p>

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Ryu, Hye-Yeon, Je-Seong Kwon, Jeong-Hak Lim, A.-Hyeon Kim, Su-Jin Baek, and Jong-Wook Kim. "Development of an Autonomous Driving Smart Wheelchair for the Physically Weak." Applied Sciences 12, no. 1 (2021): 377. http://dx.doi.org/10.3390/app12010377.

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People who have difficulty moving owing to problems in walking spend their lives assisted by wheelchairs. In the past, research has been conducted regarding the application of various technologies to electric wheelchairs for user convenience. In this study, we evaluated a method of applying an autonomous driving function and developed an autonomous driving function using ROS. An electric wheelchair with a control unit designed to enable autonomous driving was used to test the basic performance of autonomous driving. The effectiveness of the technology was confirmed by comparing the results of autonomous driving with those of manual driving on the same route. It is expected that the evaluation and improvement of the usability and ride quality as well as additional studies will help improve the mobility convenience of physically disabled persons.

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Zhu, Chao, Yi-Han Chiang, Abbas Mehrabi, Yu Xiao, Antti Yla-Jaaski, and Yusheng Ji. "Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving." IEEE Transactions on Vehicular Technology 68, no. 9 (2019): 9038–48. http://dx.doi.org/10.1109/tvt.2019.2924911.

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Berta, G. L., E. Durelli, and I. Nymann. "Simulation models for hybrid buses." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 212, no. 1 (1998): 59–72. http://dx.doi.org/10.1243/0954407981525795.

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Design and production of hybrid buses in Italy are assisted by a range of engineering tools that enable simulation and optimization of the hybrid bus performance. A review of these tools is given, involving the study and development of typical driving patterns and behaviour, the mathematical models of the components in the hybrid powertrain and, finally, the application of the computer codes. Initially simulation is a design tool. Moreover, it is found that simulation is useful for verifying the suitability of hybrid buses for the required service. By means of more or less complex algorithms, customers can be advised to adapt bus lines and to adjust powertrain parameters.

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Wang, Jiao, and Dirk Soffker. "Bridging Gaps Among Human, Assisted, and Automated Driving With DVIs: A Conceptional Experimental Study." IEEE Transactions on Intelligent Transportation Systems 20, no. 6 (2019): 2096–108. http://dx.doi.org/10.1109/tits.2018.2858179.

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Wang, Jinyang, Xiao Lin, and Hongying Yu. "POAT-Net: Parallel Offset-Attention Assisted Transformer for 3D Object Detection for Autonomous Driving." IEEE Access 9 (2021): 151110–17. http://dx.doi.org/10.1109/access.2021.3127234.

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Berger, Melanie, Debargha Dey, Bahareh Barati, Bastian Pfleging, and Regina Bernhaupt. "Designing for Collaborative Non-Driving Related Activities in Future Cars: Fairness and Team Performance." Proceedings of the ACM on Human-Computer Interaction 7, MHCI (2023): 1–28. http://dx.doi.org/10.1145/3604249.

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With the gradual transition towards assisted and automated driving, the car will transform into a more social environment where passengers and drivers engage in Non-Driving-Related Activities (NDRA). To support collaboration among occupants in future vehicles, research suggests interactive systems controlled by several users at once. In this paper, we explore five concepts for the collaborative performance of NDRA with the use-case of music playlist creation. While prior work investigated the effect on social connectedness, we expand insights towards team performance and fairness. Results from a mixed-subject experiment (N=27) show that the concepts have major consequences on team performance and fairness. Certain concepts can promote or hinder coordination effectiveness and, in turn, impact intra-vehicular collaboration. Our observations also indicate that fairness is key to fostering social collaboration in AVs, while it does not naturally define a high team performance. Subsequently, we provide recommendations to guide future designs of collaborative NDRAs in vehicles.

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Wang, Weichen, Junqiu Li, Xiaohan Li, Zhichao Li, and Ningyuan Guo. "Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving." Applied Sciences 13, no. 13 (2023): 7769. http://dx.doi.org/10.3390/app13137769.

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For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.

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Liu, Xiao, Yuanwei Liu, Yue Chen, and Lajos Hanzo. "Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach." IEEE Transactions on Vehicular Technology 69, no. 8 (2020): 8329–42. http://dx.doi.org/10.1109/tvt.2020.2996187.

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Iclodean, Călin-Doru, Bogdan-Manolin Jurchis, Cristian-Marius Macavei, Edmond-Roland Volosciuc, and Andrei-George Iclodean. "Optimizing the Energy Efficiency of Electric Vehicles in Urban and Metropolitan Environments According to Various Driving Cycles and Behavioral Conditions." Electronics 14, no. 11 (2025): 2224. https://doi.org/10.3390/electronics14112224.

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Electric vehicles are transforming urban and metropolitan transportation, providing significant benefits to both the environment and society. However, the integration of electric vehicles necessitates a well-planned infrastructure, including a sufficient number of charging stations distributed at the local level, policies that encourage the purchase and operation of electric vehicles, and the active participation of local governments and the automotive industry. Investments in improved car technologies, as well as renewable energy sources, will be critical in the shift to more sustainable metropolitan regions that have reduced pollution. Computer simulation based on virtual models performs an important role in the optimization of urban and metropolitan traffic by allowing for the rapid prototyping of real vehicle models, as well as the implementation of a wide range of test scenarios in real time. Assisted driving functions are critical in adjusting optimal driving behaviors to each of the particular scenarios of urban and metropolitan traffic. The situations discussed in this study were derived from real-world traffic and implemented and simulated on virtual models in the CarMaker version 12 application. To calibrate electricity consumption in each of the metropolitan area’s sectors, driving cycles were embedded in the virtual model. These were allocated to component sectors based on the average travel speed and its variation.

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Patil, Anand N., and Sujata V. Mallapur. "Novel Authenticated Strategy for Security Enhancement in VANET System using Block Chain Assisted Novel Routing Protocol." Scalable Computing: Practice and Experience 25, no. 1 (2024): 593–605. http://dx.doi.org/10.12694/scpe.v25i1.2406.

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In recent days, the fast growth of Vehicular Ad-Hoc Network has made smart driving practicable. The VANET performs communication with other vehicles with the intention of improving traffic flow and eliminating accidents and road hazards. VANET is nonetheless susceptible to security attacks by malicious users because of the open wireless nature of the communication channels. As a result, in this proposed system, the novel authenticated strategy for security enhancement in VANET system using block chain assisted novel routing protocol is implemented to prevent from vulnerable attacks. When vehicle enters a new roadside zone, it must continue the reauthentication procedure using the current RSU, which lowers the VANET system's overall effectiveness. Consequently, those mentioned problems are solved via blockchain technology. Reauthentication is effectively accomplished through safe authentication code transmission between the succeeding RSUs thanks to the decentralized nature of blockchain technology. The proposed system's reliability robustness against numerous harmful threats assures that it provides superior security. Furthermore, the Horse Optimization Algorithm based routing protocol assisted with blockchain technology used in this approach significantly reduce the computational and communication cost when compared to traditional approaches. To evaluate the effectiveness of blockchain assisted routing protocol, performance parameters such as PDR, routing overhead, throughput, communication and computational cost with varying scalabilities are measured. According to the findings, the proposed system works better than other existing approaches. In addition to this, our proposed framework substantially guarantees a secure and trustworthy vehicular environment with user privacy preserved.

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Napiórkowska, Anna, Piotr Zaborek, Marzanna Katarzyna Witek-Hajduk, and Anna Grudecka. "Individual Cultural Values and Charitable Crowdfunding: Driving Social Sustainability Through Consumer Engagement." Sustainability 17, no. 11 (2025): 5164. https://doi.org/10.3390/su17115164.

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Background: Charitable crowdfunding platforms have become widely used tools for raising funds to support social and humanitarian causes. As participation in these platforms is voluntary, understanding the influence of individual motivations and cultural values is essential, particularly when engaging donors across culturally diverse regions. Within the framework of Self-Determination Theory (SDT), this study explores whether consumers’ cultural orientations influence their motivations and levels of engagement in charitable crowdfunding. Methods: Data were collected through an online survey using the Computer-Assisted Web Interviewing (CAWI) method from 680 participants recruited via Amazon Mechanical Turk. Structural equation modeling was employed to analyze the effects of three cultural dimensions—collectivism, power distance, and uncertainty avoidance—on charitable giving behavior, including their interactions with intrinsic and extrinsic motivations. Results: Collectivism was found to positively predict charitable giving, while uncertainty avoidance had a negative effect. Power distance showed a mixed influence, depending on the underlying motivational factors. Comparative analysis revealed significant regional differences, particularly between respondents from the Americas and Europe, in the strength and nature of these relationships. Conclusions: The findings underscore the importance of integrating cultural factors into digital fundraising strategies and offer new insights into how individual cultural values shape prosocial behavior in online crowdfunding environments.

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Liu, Tianbi, Shanshan Du, Chenchen Liang, Bo Zhang, and Rui Feng. "A Novel Multi-Sensor Fusion Based Object Detection and Recognition Algorithm for Intelligent Assisted Driving." IEEE Access 9 (2021): 81564–74. http://dx.doi.org/10.1109/access.2021.3083503.

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Ayou, Dereje S., and Valerie Eveloy. "Integration of Municipal Air-Conditioning, Power, and Gas Supplies Using an LNG Cold Exergy-Assisted Kalina Cycle System." Energies 13, no. 18 (2020): 4599. http://dx.doi.org/10.3390/en13184599.

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A Kalina cycle-based integration concept of municipal air-conditioning, electricity and gas is investigated thermodynamically, economically, and environmentally to reduce the carbon intensity of these supplies, with attention to hot climatic conditions. The proposed poly-generation system is driven by low-grade renewable or surplus heat, and utilizes waste exergy from liquefied natural gas vaporization for refrigeration and power augmentation. At nominal conditions (130 °C driving heat), approximately 561 and 151 kJ of refrigeration and useful power per kg of liquefied natural gas regasified are generated by the proposed system, respectively, at effective first-law and exergetic efficiencies of 33% and 35%, respectively. The Kalina sub-system condenser cryogenic heat rejection condition is found to triple the system useful electrical output compared with high ambient temperature condenser heat sinking conditions. Per million ton per annum of liquefied natural gas vaporization capacity, yearly net power savings of approximately 74 GWhe could be achieved compared to standard air-conditioning, electricity, and gas supply systems, resulting in 11.1 kton of natural gas saved and 30.4 kton of carbon dioxide-equivalent emissions avoided annually. The yearly net monetary savings would range from 0.9 to 4.7 million USD per million ton per annum of liquefied natural gas regasified at local subsidized and international electricity market prices, respectively, with corresponding payback periods of 1.7 and 2.5 years, respectively.

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Fan, Zhenquan, Xiaoyu Wang, Zijin Wang, Sijia Gao, and Sheng Lin. "Distributed variable stiffness joint assist mechanism based on laminated structure." International Journal of Advanced Robotic Systems 18, no. 6 (2021): 172988142110606. http://dx.doi.org/10.1177/17298814211060661.

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Exoskeleton technology is more and more widely used in military, human rehabilitation, and other fields, but exoskeleton assisting mechanisms have problems such as high quality, concentrated driving sources, and poor flexibility. This article proposes a distributed variable stiffness joint power-assisted mechanism based on a laminated structure, which uses a giant magnetostrictive material as the driving source and the variable stiffness source of the structure. The distributed driving is realized by multiple driving units connected in series and parallel. Firstly, the drive unit stiffness matrix is deduced, and the expression equations of the cascaded total stiffness matrix of the drive module are obtained. After the simulation study, the curve of the stiffness of a single drive unit with a magnetic field and the stiffness of multiple drive units connected in series and parallel are in the absence of the magnetic field. The change curve of the stiffness of the booster module with the number of drive units under the excitation and saturation magnetic field excitation conditions is to achieve the effect of dynamically controlling the structural stiffness of the drive unit by controlling the size of the magnetic field and to obtain a general formula through data fitting. The number of drive units required under a fixed magnetic field excitation can ensure that the error is within 5%. The research results lay the foundation for further analysis of the distributed variable stiffness joint assist technology.

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Journal articles on the topic 'COMPUTER ASSISTED DRIVING'

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