Mental workLoad Accumulation Effect of Mobile Phone Distraction in L2 Autopilot Mode

As self-driving vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated psychological load. This study uses a 2 (driving mode) * 2 (call content complexity) * 6 (driving phase) three-factor mixed experimental design to investigate the effect of these factors on the driver's psychological load by measuring the driver's performance on peripheral visual detection tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental load levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in automatic driving mode, as it increases mental load while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety.

New human-computer interaction regulations for ship safe driving based on multi-mode data interconnection

Based on the analysis of the multi-mode data of ship mechatronics and the new human-computer interaction regulations for safety driving, a new safety driving regulation based on multi-mode data is put forward. The new regulations for ship safe driving use mechanical and electrical data to form small-world data interconnection. Artificial intelligence and human-computer interaction operation information are used to integrate and communicate, and human-computer interaction data are incorporated to standardize driving behavior to integrate historical driving data, and finally, the standardized automatic self-driving is formed. The new human-computer interaction regulations formed by the safe driving system make it possible to solve and optimize the ship safe driving mode.

Research on Sailing Efficiency of Hybrid-Driven Underwater Glider at Zero Angle of Attack

The sailing efficiency of an underwater glider, an important type of marine environment detection and data collection equipment, directly affects its range and duration. The zero-angle-of-attack gliding can be achieved by adjusting the wing installation angle to minimize the drag and improve the sailing efficiency, and thus further improving performance of the glider. This paper first presents the dynamic characteristics of a hybrid-driven underwater glider with a certain wing installation angle when it is sailing at zero angle of attack in buoyancy-driven mode and hybrid-driven mode. In buoyancy-driven mode, with a given wing installation angle, the glider can achieve zero-angle-of-attack gliding only at a specific glide angle. In hybrid-driven mode, due to the use of a propulsion system, the specific glide angle that allows the zero-angle-of-attack gliding in buoyancy-driven mode is expanded to a glide angle range bounded by zero degrees. Then, the energy consumption per meter is introduced as an indicator of sailing efficiency, and the effects of glide angle and wing installation angle on sailing efficiency of the zero-angle-of-attack glider in two driving modes are studied under the conditions of given net buoyancy and given speed, respectively. Accordingly, the optimal wing installation angle for maximizing the sailing efficiency is proposed. Theoretical analysis shows that the sailing efficiency of a zero-angle-of-attack glider can be higher than that of a traditional glider. Considering the requirements of different measurement tasks, a higher sailing efficiency can be achieved by setting reasonable parameters and selecting the appropriate driving mode.

Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

Forecast of the Impact of Electric Car Battery Performance and Infrastructural and Demographic Changes on Cumulative Accessibility for the Five Most Populous Cities in Poland

The purpose of this study is to compare (1) technological factors (the ranges offered by the batteries of three popular electric vehicles in Poland); (2) infrastructure improvements; and (3) demographic changes and their impact on accessibility in the context of the ranges of labor markets within the 30, 60, and 90 min isochrones in moderate driving mode for the five largest cities in Poland using cumulative accessibility. We conclude that technological developments result in a much greater improvement in accessibility than demographic and infrastructural change. This is already visible with the 30 to 60 min isochrones, in particular when using the BMW in Cracow (with a more than 36% improvement in accessibility). Even greater changes, reaching as much as over 90%, are observed for the 60–90 min isochrones. The analysis shows that the shift in electromobility may be constrained by parallel demographic processes, dispersion of population in suburban areas, and the development of road infrastructure. The novelty of the approach stems from the fact that it is based on three above mentioned key factors that influence the accessibility of labor markets for EV users in the largest cities up to 2030.

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.

A New On-board Charging-Driving Integrated Topology for V2G Technology

The performance of batteries and on-board chargers (such as the volume in the car, energy storage capacity and charging speed) needs to be improved, which has become one of the main factors restricting the development of electric vehicles. The development of Vehicles to Grid technology puts forward higher requirements for chargers. With the development of Vehicles to Grid (V2G) technology, more realizable functions put forward higher requirements for chargers. To solve the problem of charging system in electric vehicle, a charging-driving integrated topology was designed, which makes full use of two-stator motor and inverters to be transformed to a charging system. The supercapacitor and the battery are used to form the hybrid power system. In the driving mode, the startup and acceleration performance of the vehicle are improved. In the charging mode, the various functions of Vehicles to Grid technology can be satisfied, and the electrical isolation is realized. This topology not only improves the power, but also greatly reduces the charging/discharging times of the battery, and improves the overall performance of the system. The feasibility is verified by simulation.

Research on Erecting Joint and Dynamic Characteristics of Four-Hinge-Point Dual-Drive Missile

In order to improve the quick reaction ability and stability of the missile launching erecting system, the driving mode of the erecting joint is optimized. A four-hinge-point double-driving erecting joint is proposed and the mechanical model is established, the force and time of erecting cylinder are calculated by Matlab, and compared with two kinds of three-hinge-point joint. The results show that the response speed of the four-hinged double-drive erecting joint is fast, the erecting process is fast in the early stage and stable in the later stage, and the elongation and the erecting cylinder force are between two kinds of three-hinged joints, the four-hinge erector can not only combine the advantages of the three-hinge front-mounted and rear-mounted structure, but also realize the aim of compatibility design and improve the rapidity and stability of the missile erector, it can provide an important basis for the structural design of missile launcher.

Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.

Self-Organised Critical Dynamics as a Key to Fundamental Features of Complexity in Physical, Biological, and Social Networks

Studies of many complex systems have revealed new collective behaviours that emerge through the mechanisms of self-organised critical fluctuations. Subject to the external and endogenous driving forces, these collective states with long-range spatial and temporal correlations often arise from the intrinsic dynamics with the threshold nonlinearity and geometry-conditioned interactions. The self-similarity of critical fluctuations enables us to describe the system using fewer parameters and universal functions that, on the other hand, can simplify the computational and information complexity. Currently, the cutting-edge research on self-organised critical systems across the scales strives to formulate a unifying mathematical framework, utilise the critical universal properties in information theory, and decipher the role of hidden geometry. As a prominent example, we study the field-driven spin dynamics on the hysteresis loop in a network with higher-order structures described by simplicial complexes, which provides a geometric-frustration environment. While providing motivational illustrations from physical, biological, and social systems, along with their networks, we also demonstrate how the self-organised criticality occurs at the interplay of the complex topology and driving mode. This study opens up new promising routes with powerful tools to address a long-standing challenge in the theory and applications of complexity science ingrained in the efficient analysis of self-organised critical states under the competing higher-order interactions embedded in complex geometries.