Vehicle Environment Research Articles

The gender and age differences in the passengers' thermal comfort during cooling and heating conditions in vehicles.

The thermal physiological and psychological responses in vehicles, influenced by gender and age, play a crucial role in ensuring passengers' comfort. However, these differences have often been overlooked. This study aims to comprehensively examine passengers' thermal comfort and investigate gender and age disparities based on their physiological and psychological responses. Experiments were conducted inside a vehicle placed in a climate chamber under cooling and heating conditions, with the collected data subjected to statistical analysis. The findings reveal that males had significantly higher mean skin temperatures in cooling conditions and lower skin temperatures in heating conditions than females. However, overall thermal sensation and comfort did not significantly differ between genders. Interestingly, age-related differences were observed to a limited extent in both conditions. This study provides valuable insights into passengers' thermal responses in vehicles, considering the factors of gender and age, thereby contributing to a comprehensive understanding of thermal comfort in a vehicle environment.

Stability Analysis of a Car-Following Model with Effect of Driver’s Memory Delay in Connected Vehicle Environment

Stability Analysis of a Car-Following Model with Effect of Driver’s Memory Delay in Connected Vehicle Environment

An Improved GOMP Sparse Channel Estimation for Vehicle-to-Vehicle Communications

Reliable channel estimation is critical for wireless communication performance, especially in vehicle-to-vehicle (V2V) communication scenarios. Aiming at the major challenges of channel tracking and estimating as the highly dynamic nature of vehicle environments, an improved generalized orthogonal matching pursuit (iGOMP) is proposed for V2V channel estimation. The iGOMP algorithm transforms the channel estimation problem into a sparse signal recovery problem and replaces the classical inner product criterion with the Dice atom matching criterion. Additionally, the atomic weak progressive selection method is integrated to avoid the suboptimal selection of atoms from the redundant dictionary using the inner product criterion. The proposed iGOMP method can achieve optimal channel estimation by iterating feedback results. Simulation results demonstrate that the iGOMP method has superior estimation accuracy, mean square error (MSE), and bit error rate (BER) performance compared with traditional channel estimation methods in V2V communications.

Intrusion detection system for cyberattacks in the Internet of Vehicles environment

This paper presents a novel framework for intrusion detection specially designed for cyberattacks, such as Denial-of-Service, Distributed Denial-of-Service, Distributed Reflection Denial-of-Service, Brute Force, Botnets, and Sniffing, on vehicles that are situated in the Internet of Vehicles environment. We propose an intrusion detection system based on machine learning that is capable of detecting abnormal behavior by examining network traffic to find unusual data flows. In this paper, we have presented a strategy for intrusion detection through a careful evaluation and selection of the most effective techniques for the following steps of the machine learning process: (i) data preprocessing by using Z-score normalization that preserves the data distribution for the proposed method and handles outliers; (ii) feature selection by using a regression model that simplifies the model complexity and reduces the execution time; and (iii) model selection and training – Random Forest, Extreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine – with hyperparameter optimization to control the behavior in the training phase and to prevent overfitting. The effectiveness of the proposed solution is demonstrated by extensive numerical experiments carried out using the well-known standard datasets CIC-IDS-2017, CSE-CIC-IDS-2018, and CIC-DDoS-2019, both separately and merged. We achieved a high accuracy above 99.8% within a running time of 46.9 s and 0.24 s detection time for the three combined intrusion detection system datasets, thereby showing that the proposed intrusion detection system outperforms the previous methods introduced in the literature.

Dynamic path planning in autonomous driving

With the development of emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) and fifth-generation cellular network technology (5G), good conditions are provided for the level of automation, providing the necessary assistance to coordinate the environment, planning decisions and path optimisation for self-driving vehicles. This paper briefly outlines and analyses the key technologies required to realize autonomous driving and summarizes the corresponding research results for each part; by considering the operation process of self-driving vehicles, this paper chooses path planning as the focus of research and presents it in the form of a methodology; finally, it concludes with a summary of personal opinions and a discussion of possible future research directions.

Evaluation of the Connected Vehicle Environment Effectiveness at Tunnel Entrance

Evaluation of the Connected Vehicle Environment Effectiveness at Tunnel Entrance

OPTIMAL LONG-RANGE-WIDE-AREA-NETWORK PARAMETERS CONFIGURATION FOR INTERNET OF VEHICLES APPLICATIONS IN SUBURBAN ENVIRONMENTS

In this paper, the effect of Long-Range wireless technology parameters on signal propagation in suburban environments is investigated. Wireless propagation modeling provides information about the wireless channel and its impact on communication links. Received signal strength and coverage area are evaluated to determine signal path loss. The operating frequency of 433 MHz Long Range Wireless Area Network is utilized with different spreading factors, bandwidths, and code rates. Empirical propagation models are utilized to predict a mathematical model based on measured empirical signal strength in a suburban site in Baghdad City. The measured signal strength and signal-to-noise ratio values were obtained through drive tests in an Internet of Vehicles environment to design a network that could accurately report vehicle locations. The LoRa parameters affected the calculated path loss exponent, leading to various predictions in the network design. The path loss exponent exhibited instability due to the presence of obstacles and different long-range parameter settings. Path loss exponent deviation fluctuates due to bandwidth and spreading factor variations. Path loss exponent reduced at higher coding rates for more protection purposes. Packet ratio reception improved as the coding rate increased. To minimize the impact of the path loss on network design, an optimization policy was employed to determine the best parameters that resulted in the lowest path loss. The optimal path loss obtained at LoRa configuration parameters settings with spreading factor (7), bandwidth (500 kHz), and code rate (4/5).

Secure Digital Twin Migration in Edge-Based Autonomous Driving System

Digital twin technology is being applied increasingly in the Internet of Vehicles environment, but it still faces many challenges in terms of efficiency and security. In the field of digital twin-based autonomous driving, many previous works have been done to study efficient migration methods of digital twin models. But these works consider the migration process as a blackbox. We study the efficient migration method of the digital twin model between the edge computing nodes inside the blackbox. We propose three different migration strategies depending on the source of the initial data and the source of the updated data, and evaluate the efficiency of these strategies in terms of migration time in different network environments using the autonomous driving simulation platform CARLA. We then derive methods for selecting migration strategies under different network conditions. During the migration process, there may be external attacks on participating elements or networks. We analyze the security problems that may arise during the migration process and propose corresponding defense methods against such cyber attacks.

Effect of temperature on VOC emissions and odor from vehicle carpet

Volatile organic compounds (VOCs) released from carpets deteriorate the air quality in the confined vehicle interior. The wide range of temperatures experienced by vehicle carpets impacts VOC and odor emissions, affecting the physical and mental health of occupants. We investigated the VOC emissions and odor characteristics of vehicle carpets at 25, 40, 50, and 60 °C, respectively. An approach was developed for simultaneous objective sampling and subjective evaluation in a 1 m3 chamber with controlled temperature, humidity, and ventilation. Sensory panelists were screened and trained to evaluate the odor intensity, preference, and type. The emitted VOC types and concentrations increased with temperature; there were 4.5 times more VOC types at 60 °C than at 25 °C. The emission of aromatic compounds, heterocyclic compounds, acids, esters, and ketones significantly increased with temperature. The interaction between odorants affected odor characteristics at the four temperatures; the dominant odors were plastic-like and stinky at 25 °C, rubber- and leather-like at 40 and 50 °C, and aroma at 60 °C. Further quantitative and qualitative exploration of substances, odors, and health effects from carpet emissions at different temperatures could help improve the vehicle environment.

Utilizing MOSFET - Based Buck - Boost Converter to Ensure the Operation of New Energy Vehicle Battery System at Low Temperature

This article proposes a MOSFET-based buck-boost DC/DC converter solution to the problem of battery output voltage drop in low-temperature environments for electric vehicles. At low temperatures, the chemical reaction rate of the battery slows down, resulting in a drop in output voltage, which cannot provide a stable voltage for the drive system. To solve this problem, it is proposed to add a buck-boost DC/DC converter between the positive and negative poles of the battery, and use the high-speed switching characteristics of MOSFET to adjust the battery output voltage in real time through duty cycle control. When the temperature is too low, increase the duty cycle to obtain a higher output voltage, and provide a stable working voltage for the drive motor through inversion. At the same time, the output of the converter can also be used for battery power to achieve self-heating, helping the battery quickly return to normal operating temperature.

The application of advanced computational algorithms used for cooperative communication transmission of vehicular networks: a proposed method

Telematics will be one of the critical technologies in the future intelligent transportation system and establish communication between vehicles and vehicles, vehicles and networks, and vehicles and people. Thus, vehicles can sense mobile environments and make rational driving decisions. Therefore, the safety and efficiency of traffic flow would be enhanced. However, due to the unknown nature and higher complexity of the connected network environments of vehicles, the utilization of conventional optimization theory fails to generate satisfying results. To address the problem, this article proposes a methodology for collaborative transmission for communication regarding the Internet of Vehicles (IoV) with the help of advanced computational algorithms. The article employs a multi-intelligence advanced computational algorithm to construct a collaborative communication transmission mechanism in the telematics communication system model. The proposed algorithm fully considers the vehicle mobility and quality-of-service (QoS) of telematics services within the network slice. It adjusts the slice’s radio resource allocation and parameter settings on an expanded time scale to improve the QoS of telematics services and increase the system’s long-term revenue. The simulation results show that the proposed algorithm has a more significant performance improvement than conventional algorithms using telematics information transmission. For example, when the same load conditions are under consideration, the total capacity of the vehicle-to-infrastructure (V2I) link optimized by the proposed algorithm is still higher than that of the other three baseline strategies.

Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications

With the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.

Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle's environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous-driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights was collected from training on the individual datasets, their merged versions, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the datasets mentioned earlier and their subsets. The evaluation revealed that using custom datasets for training significantly improved the detection performance compared to the YOLOv8 base weights. Furthermore, using more images through the feature-related data merging technique steadily increased the object detection performance.

Research on unmanned transfer vehicle path planning for raw grain warehousing

A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment.

Coupling Control of Traffic Signal and Entry Lane at Isolated Intersections Under the Mixed-Autonomy Traffic Environment

There is a growing number of studies on the traffic control strategies of signal timings and vehicle trajectories at signalized intersections, while lane assignments are widely pre-specified and fixed. Meanwhile, existing strategies generally require a fully connected and automated vehicles (CAVs) environment. To fill up the gaps, this study contributes to a two-dimensional (spatiotemporal) control strategy by jointly optimizing traffic signals, lane settings, and vehicle trajectories at isolated signalized intersections under the mixed traffic of connected automated and human-driven vehicles. Specifically, based on the pseudo-platoons, signal timing plans and settings of approach lanes are jointly optimized by a piece-wise linear programming model. Then, vehicle trajectory control is integrated into the collaborative control framework to smooth vehicle trajectories. Three groups of numerical experiments are conducted to verify the effectiveness and efficiency of the proposed control method. Results show that the proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.

An Extended Multilane Lattice Hydrodynamic Model Considering the Predictive Effect of Drivers under Connected Vehicle Environment

An Extended Multilane Lattice Hydrodynamic Model Considering the Predictive Effect of Drivers under Connected Vehicle Environment

Upper cervical spine bone mineral content variations in elderly females

The purpose of the study was to determine the bone mineral densities (BMDs) of the C1 and C2 vertebrae and discuss their implications for autonomous vehicle environments and vulnerable road users. Using quantitated computed tomography (QCT), the BMDs were obtained at eight regions for the C1 vertebra and seven regions for the C2 vertebra. The spine surgeon author outlined the boundaries of each region, and nine elderly female human cadaver specimens were used. The regions were based on potential stabilization locations for fracture fixation. In the C1 vertebra, the BMD was greatest at the anterior tubercle, followed by the posterior tubercle, the posterior arch, and the lateral and anterior lateral masses. In the C2 vertebra, the distal odontoid had the greatest BMD, followed by the spinous process, the C2-lateral mass, the odontoid-body interface, and the anterior inferior aspect of the body. Use of these data in female-specific finite element models may lead to a better understanding of load paths, injuries, mechanisms, and tolerance.

Carbon Emission Impacts of Longitudinal Disturbance on Low-penetration Connected Automated Vehicle Environments

Carbon Emission Impacts of Longitudinal Disturbance on Low-penetration Connected Automated Vehicle Environments

Effect of heat mitigation strategies on the thermal environment of hardened pavement and pedestrian thermal comfort in a hot and humid area: A case study of Guangzhou, China.

The synergistic effect of heat waves and urban heat islands (UHI) adversely affect the urban environment, building energy consumption, and human health. The absorption and storage of short-wave radiation by hardened pavement is the primary reason for high-temperature heat waves and the UHI effect. Three mitigation strategies were assessed to alleviate the influence of high-temperature hardened pavement on the thermal environment and thermal comfort of pedestrians, water-spray vehicles (spray) and pavement watering (watering) to take advantage of evaporative cooling, and a fan based on convective heat transfer. In addition, the fan-spray and fan-watering combinations were evaluated to increase the evaporative cooling rate. Field measurements showed that the five heat mitigation strategies improved the thermal environment and reduced environmental thermal stress. The thermal sensation, comfort sensation, and mean skin temperature (MST) were analyzed in thermal perception experiments. The result indicated that the mitigation strategies changed the subjects' thermal perception. Moreover, regression analysis between the universal thermal climate index (UTCI) and the thermal response showed that the neutral temperature and the range of comfort threshold of UTCI and MST increased after implementing the mitigation strategies.

SIL Setup for Motor Controller Testing in Virtual Vehicle Environments

This research paper explores the implementation and benefits of Software-in-the-Loop (SIL) setups for testing motor controllers within virtual vehicle environments. SIL testing offers significant advantages in terms of cost, efficiency, and safety compared to traditional hardware-based testing methods. This paper discusses the key components of SIL setups, their implementation strategies, and case studies demonstrating their effectiveness in real-world applications. Additionally, challenges and future research directions in SIL testing for motor controllers are also addressed.