Vehicle Control Research Articles

Taxifolin attenuates hepatic ischemia-reperfusion injury by enhancing PINK1/Parkin-mediated mitophagy.

Hepatic ischemia-reperfusion (I/R) injury stands as a recurring clinical challenge in liver transplantation, leading to mitochondrial dysfunction and cellular imbalance. Mitochondria, crucial for hepatocyte metabolism, are significantly damaged during hepatic I/R and the extent of mitochondrial damage correlates with hepatocyte injury. PINK1/Parkin-mediated mitophagy, is a specialized form of cellular autophagy, that maintains mitochondrial quality by identifying and removing damaged mitochondria, thereby restoring cellular homeostasis. Taxifolin (TAX), a natural flavonoid, possesses antioxidant, anti-inflammatory and anticancer properties. This study aimed at investigating the effects of TAX on hepatic I/R and the underlying mechanisms. C57BL/6 mice were pretreated with TAX or vehicle control, followed by 60 minutes of 70% hepatic ischemia. After 6 hours of reperfusion, the mice were euthanized. In vitro, TAX-pretreated primary hepatocytes were subjected to oxygen glucose deprivation/reperfusion (OGD/R). Hepatic I/R caused mitochondrial damage and apoptosis in hepatocytes, but TAX pretreatment mitigated these effects by normalizing mitochondrial membrane potential and inhibiting reducing apoptotic protein expression. TAX exerted its protective effects by enhancing mitophagy via the PINK1/Parkin pathway. Moreover, silencing the PINK1 gene in primary hepatocytes reversed the beneficial effects of TAX. The results of the study demonstrate that promoting mitophagy through the PINK1/Parkin pathway restores mitochondrial function and protects the liver from I/R, suggesting that it may have therapeutic potential for the treatment of hepatic I/R.

Autonomous Vehicle Data Protection : A Review of Security Threats, Challenges and Protective Mechanism

The swift advancement of autonomous vehicle (AV) technology has presented notable obstacles with data security and privacy. This analysis examines major security threats that jeopardize user privacy and vehicle control, including data breaches, virus penetration, cyber attacks, illegal access, V2V and V2I system hacking, GPS spoofing, and data leaks. It also looks at the challenges developers have when putting strong security measures in place, considering how complicated AV infrastructures may be and how cloud, IoT, and AI are integrated. The paper outlines a number of defence tactics, such as blockchain technology, machine learning for anomaly detection, encryption, and secure communication protocols. In order to improve AV cyber security, it ends with suggestions for further study in fields like edge computing and quantum-resistant cryptography.

Comparing effects of terpene-based deep eutectic solvent and solid microneedles on skin permeation of drugs with varying lipophilicity

Transdermal delivery of therapeutic molecules is often hindered by the properties of the skin, with the stratum corneum serving as the primary permeation barrier. To overcome this barrier, the integrity of the stratum corneum can be modified by chemical permeation enhancers, such as deep eutectic solvents (DESs), or by mechanically impairing the skin with microneedles (MNs). However, a systematic comparison between these strategies is currently lacking. Hence, this study examined the potential of DESs and MNs to promote the permeation and retention of drugs with varying lipophilicities – specifically, the hydrophilic drug metronidazole (logP ∼ 0), the moderately lipophilic drug lidocaine (logP ∼ 2.3), and the highly lipophilic drug clotrimazole (logP ∼ 5). A mixture of menthol and thymol was selected as a model terpene-based DES and delivery vehicle, while a DermaPen equipped with solid MNs was used to mechanically impair the skin. Permeation rates of model drugs applied to the skin with either DES, MNs, or both were compared to the rates determined for the drugs applied in control vehicles. Both strategies were found to compromise the skin barrier function, but their permeation-enhancing effect was dependent on the lipophilicity of tested model drug. The DES was most effective for the hydrophilic drug metronidazole, while the MNs were more effective in increasing the permeation of the highly lipophilic drug clotrimazole. For the moderately lipophilic drug lidocaine, neither the DES nor microneedles increased its permeation rate, as the drug permeated through the skin well on its own. Notably, the combination of both enhancement strategies did not result in significantly better permeation rates of the drugs compared to the individual approaches. In conclusion, both the terpene-based DES and solid MNs are effective strategies to enhance drug permeation through the skin, but our results suggest that the choice of strategy should be dictated by the drug’s lipophilicity. Moreover, from a permeation-enhancing perspective, there is no benefit in combining these two strategies.

Path following control of under-actuated autonomous surface vehicle based on random motion trajectory dataset and offline reinforcement learning

Path following control of under-actuated autonomous surface vehicle based on random motion trajectory dataset and offline reinforcement learning

NMPC formation control for heterogeneous multi-air vehicles with obstacle avoidance and connectivity maintenance

NMPC formation control for heterogeneous multi-air vehicles with obstacle avoidance and connectivity maintenance

PID control for the water-air hybrid aquatic-aerial vehicle’s exit based on simulated annealing genetic algorithm optimization

Abstract A hybrid aquatic-aerial vehicle is a special equipment with the ability to perform amphibious operations in water and air, and its control accuracy and stability during the water outflow process are crucial for ensuring the safety and operational efficiency of the vehicle. This article first constructs the kinematic and dynamic models of the hybrid aquatic-aerial vehicle and designs the basic control strategy based on a PID controller. Given the limitations of a single PID control strategy in specific contexts, this study proposes a PID controller based on genetic algorithm optimization and introduces a simulated annealing algorithm in the optimization process of the genetic algorithm to make the adjustment of controller parameters more precise and efficient. Through simulation verification, it was found that the optimized PID controller improved the performance of the aircraft during the outflow stage, indicating that this optimization control strategy has a positive impact on improving the operational performance of the aircraft.

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

Trans-Ferulic acid reduces the sedative activity of diazepam in thiopental sodium-induced sleeping mice: A potential GABAergic transmission.

trans-Ferulic acid (TFA), a bioactive compound found in many plants, has been recognized for its diverse pharmacological activities, including potential neurological benefits. Previous studies suggest that TFA exerts anxiolytic effects via GABAergic pathways. This study aimed to investigate the sedative effects of TFA and its possible molecular mechanisms through in vivo and in silico approaches. Adult Swiss mice were randomly divided into six groups (n=7): control (vehicle), standard (DZP: Diazepam at 3mg/kg, p.o.), three test groups (TFA at 25, 50, and 75mg/kg, p.o.), and a combination group (TFA: 50mg/kg with DZP: 3mg/kg, p.o.). Thirty minutes post-treatment, thiopental sodium (TS) at 40mg/kg was administered to induce sedation, and latency as well as duration of sleep, were observed for up to 4h. In silico studies were conducted with GABAA receptor subunits (α1 and β2) to elucidate the possible molecular interactions. The results demonstrated that TFA significantly reduced latency and extended sleep duration in a dose-dependent manner compared to the control. Additionally, TFA combined with DZP further significantly (p<0.001) enhanced these effects. In silico analysis revealed that TFA and DZP exhibited strong binding affinities with the GABAA receptor subunits (α1 and β2) in the identical binding sites, with binding energies of -6.8 and-8.7kcal/mol, respectively. Collectively, TFA exerted a mild sedative effect in TS-induced sleeping mice and modulated the activity of DZP, likely through interactions with GABAA receptors. TFA showed promising activity as a potential candidate for managing sleep disorders such as insomnia.

Aerosolized Harmful Algal Bloom Toxin Microcystin-LR Induces Type 1/Type 17 Inflammation of Murine Airways

Harmful algal blooms are increasing globally and pose serious health concerns releasing cyanotoxins. Microcystin-LR (MC-LR), one of the most frequently produced cyanotoxins, has recently been detected in aerosols generated by the normal motions of affected bodies of water. MC-LR aerosol exposure has been linked to a pro-inflammatory influence on the airways of mice; however, little is understood about the underlying mechanism or the potential consequences. This study aimed to investigate the pro-inflammatory effects of aerosolized MC-LR on murine airways. C57BL/6 and BALB/c mice were exposed to MC-LR aerosols, as these strains are predisposed to type 1/type 17 and type 2 immune responses, respectively. Exposure to MC-LR induced granulocytic inflammation in C57BL/6 but not BALB/c mice, as observed by increased expression of cytokines MIP-1α, CXCL1, CCL2, and GM-CSF compared with their respective vehicle controls. Furthermore, the upregulation of interleukins IL-17A and IL-12 is consistent with Th1- and Th17-driven type 1/type 17 inflammation. Histological analysis confirmed inflammation in the C57BL/6 lungs, with elevated neutrophils and macrophages in the bronchoalveolar lavage fluid and increased pro-inflammatory and pro-resolving oxidized lipids. In contrast, BALB/c mice showed no significant airway inflammation. These results highlight the ability of aerosolized MC-LR to trigger harmful airway inflammation, requiring further research, particularly into populations with predispositions to type 1/type 17 inflammation.

Adaptive Synthesized Fault-Tolerant Autonomous Ground Vehicle Control With Guaranteed Performance and Saturated Input

Adaptive Synthesized Fault-Tolerant Autonomous Ground Vehicle Control With Guaranteed Performance and Saturated Input

Valifenalate-induced non-adverse thyroid changes via adaptive induction of uridine 5′-diphospho-glucuronosyltransferase (UGT) in the liver of dogs and rats but not humans

Some rat and dog toxicology studies with the fungicide valifenalate showed minimal, non-adverse thyroid changes, mostly above the maximum tolerated dose, and concomitantly with liver effects. This publication describes their mode of action (MOA), combining in vivo and new approach methodologies (NAMs), in a weight of evidence approach.Data demonstrate a MOA of liver enzyme induction via nuclear receptor CAR/PXR activation, increased thyroxine (T4) metabolism and elevated thyroid stimulating hormone (TSH) level, leading to thyroid follicular cell hypertrophy and increased thyroid weight. Non-human relevance of the MOA was demonstrated in in vitro cross species assays in rat, dog and human hepatocytes. Increased gene expression and activity of cytochrome P450s (CYPs) and uridine 5′-diphospho-glucuronosyltransferases (UGTs) were observed in rat and dog hepatocytes exposed to valifenalate, with increased T4 clearance and/or T4 glucuronidation/T4-UGT activity. Therefore, a causal relationship between increased liver enzyme induction and thyroid effects in dogs and rats is concluded. Rat hepatocytes were most sensitive, while valifenalate did not increase T4-UGT activity above 2-fold of vehicle control or T4 glucuronidation and T4 clearance in human hepatocytes. Consequently, valifenalate exposure in humans is unlikely to lead to decreased T4 levels, and subsequent thyroid and developmental neurotoxicity effects. Alternative human-relevant thyroid MOAs were excluded, i.e. inhibition of deiodinases (DIO), thyroperoxidase (TPO) or the sodium iodide symporter (NIS).Due to known species differences in thyroid homeostasis between humans and laboratory animals and, importantly, based on the presented data, this liver enzyme mediated MOA is considered not relevant for human hazard assessment.

ANTIDEPRESSANT-LIKE ACTIVITY OF AQUEOUS EXTRACT OF ROSA DAMASCENA IN MICE

Objective: Plant-based drugs have the potential to be very effective substitutes for prescription antidepressants. Rosa damascena has therapeutic potential as an analgesic, anticonvulsant, antitussive, bronchodilatory, antibacterial, anti-diabetic, anti-inflammatory, antioxidant, and laxative. Given this context, the goal of the current study was to assess Rosa damascena's potential antidepressant effects. Methods: Maceration was used to create an aqueous extract of Rosa damascena. The Tail Suspension Test (TST) on BALB/c mice and the Forced-Swimming test (FST) on C57BL/6 mice were used to quantify the antidepressant activity. Mice were divided into three groups: control (saline), standard (citalopram and desipramine), and Rosa damascena aqueous extract (n = 6 per group). Intraperitoneally (1 ml/100 g) injections of drugs were administered. Analysis of variance was used to examine the data, and then LSD post-hoc tests were performed. The data are expressed as mean±SEM. Results: Antidepressant-positive controls, citalopram and desipramine, significantly decreased the time of immobility in the FST and TST as compared to the vehicle control group (p&lt;0.001). In FST, the immobility durations were significantly reduced by the Rosa damascena aqueous extract at a dose of 40 mg/kg compared to lesser doses of the same extract (10 and 20 mg/kg) (p&lt;0.001). Similarly, the 40 mg/kg dose of Rosa damascena aqueous extract significantly reduced the length of immobility in TST (p&lt;0.001). Conclusion: The present findings demonstrate Rosa damascena's antidepressant-like effects in mice. Further research is necessary to determine the underlying mechanism by which Rosa damascena generates effects akin to those of an antidepressant in light of this observation.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Steering-Angle Prediction and Controller Design Based on Improved YOLOv5 for Steering-by-Wire System.

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.

Sodium Benzoate Induces Fat Accumulation and Reduces Lifespan via the SKN-1/Nrf2 Signaling Pathway: Evidence from the Caenorhabditis elegans Model.

Sodium benzoate (SB) is widely used in food products, cosmetics, and medical solutions due to its antimicrobial properties. While it is generally considered safe and has potential neuroprotective benefits, SB has also been linked to adverse effects, including hepatic oxidative stress and inflammation. However, the potential effects of SB on obesity and lifespan remain poorly understood. In this study, we investigated the effects of SB on fat accumulation and lifespan using the nematode Caenorhabditis elegans (C. elegans) as a model system. Wild-type worms were exposed to various SB concentrations (0%, 0.0004%, 0.0008%, 0.004%, and 0.1%) and 0.016% glucose as a positive control for 72 h in liquid or on NGM agar plates. Fat accumulation was assessed through the Oil Red O staining, which revealed that SB induced more fat accumulation compared to vehicle control, even at low concentrations, including the dosage of 0.0004%. Lifespan analysis also demonstrated that SB significantly reduced lifespan in wild-type worms, even at low concentrations. Further investigations found that SKN-1 (an Nrf2 homolog) is necessary for SB-induced fat accumulation and lifespan reduction. Moreover, SB inhibited the nuclear localization of SKN-1 under oxidative stress conditions. These findings suggest that SB may induce fat accumulation and reduce lifespan by inhibiting the oxidative stress-mediated SKN-1 signaling pathway.

Fabrication of Remotely Controllable Robotic Weed-Ejector Vehicle

Weed is an unwanted plant in an agriculture field that keeps the crop plants deprived of sunlight, fertilisers and water. If not removed, weed reduces the crop output to a larger extent i.e., endangering the farmer's interests. There are three main reasons for leaving weed to grow in fields and substantial loss of crop output 1) non-availability of agricultural labour, 2) fear of snake bites and 3) higher amounts of time required for weed injection. As of there are no successful ROVs (Remotely operating vehicles) to get to the weed and capable of pulling off the weed without the farmer physically entering into the agricultural fields. The Proposed model reduces farmers getting exposed to snake bites during weed ejection and reduces the time and labour requirement for weed ejection. The project involves the study of different mechanisms required for weed pull-off from the agricultural fields and remote-control systems, modelling of components, fabrication of components, assembly and testing of the Remotely Controllable Robotic Weed-Ejector Vehicle.

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Vaping/e-cigarette-induced pulmonary extracellular vesicles contribute to exacerbated cardiomyocyte impairment through the translocation of ERK5

AimsThe impact of e-cigarettes/vaping on cardiac function remains contradictory owing to insufficient direct evidence of interorgan communication. Extracellular vesicles (EVs) have protective or detrimental effects depending on pathological conditions, making it crucial to understand their role in lung-cardiac cell interactions mediated by vaping inhalation. Methods and key findingsPulmonary EVs were characterized from animals that underwent 12 weeks of nicotine inhalation (vaping component) (EVsNicotine) or vehicle control (EVsVehicle). EVsNicotine significantly increased in size and abundance compared with EVsVehicle. The direct effect of EVs Nicotine and EVs Vehicle on cardiomyocytes was then assessed in vitro and in vivo. EVs Nicotine led to a decrease in cardiac function as manifested by reduced cardiac contractility and impaired relaxation. EVs Nicotine induced increased levels of cleaved caspase-1 and cleaved caspase-11 in cardiomyocytes, indicating the promotion of pyroptosis. Meanwhile, EVsNicotine stimulated the secretion of fibrotic factors. Further analysis revealed that nicotine inhalation stimulated EVs Nicotine enriched with high levels of ERK5 (EVs Nicotine-ERK5). It was discovered that these EVs derived from pulmonary epithelial cells. Furthermore, inhibiting cardiac ERK5 blunted the EVs Nicotine-induced pyroptosis and fibrotic factor secretion. We further identified GATA4, a pro-pyroptosis transcription factor, as being activated through ERK5-dependent phosphorylation. SignificanceOur research demonstrates that nicotine inhalation exacerbates cardiac injury through the activation of EVs derived from the lungs during e-cigarettes/vaping. Specifically, the EVs containing ERK5 play a crucial role in mediating the detrimental effects on cardiac function. This research provides new insights into the cardiac toxicity of vaping and highlights the role of EVs Nicotine-ERK5 in this process.

Integrated automatic parking path planning and trajectory tracking optimization method

Current automatic parking systems often separate path planning and trajectory tracking, increasing complexity and failing to meet the parking needs of autonomous cars. Consequently, this study presents an approach based on Multi-stage Nonlinear Model Predictive Control (MSNMPC) for integrated autonomous parking path planning and trajectory tracking optimization. The method represents the evolution of uncertainty parameters over time through a scenario tree, and the MSNMPC controller with a predictive horizon P defines P + 1 phases, each with specific cost and constraint functions that depend only on the vehicle state and control inputs of that phase to satisfy the constraints of all phases of the parking process. In addition, the method integrates path planning and tracking control into one optimization problem, which is solved online to achieve integrated parking control. Simulation confirms that, in comparison to MPC with RRT* hierarchical control, the integrated parking scheme of MSNMPC has less lateral error and offers superior flexibility and tracking performance. To verify the generality of the scheme, it was validated in the diagonal parking scenario and the vertical parking scenario, respectively. The results show that, compared to the control experiments, the parking elapsed time has been reduced by 10.76% and 9.02%, respectively, enhancing parking efficiency. In addition, the parking error has decreased by 30.77% and 38.46%, respectively, thus improving parking accuracy. Moreover, the minimum safe distance in the control scheme for addressing uncertainty factors is 0.7 m greater than that of the control experiments, meeting the driving requirements for driverless vehicles in parking scenarios.