Vehicle Lateral Stability Control Based on Stability Category Recognition With Improved Brain Emotional Learning Network

Wang, Hongbo; Zhou, Juntao; Hu, Chenglei; Chen, Wuwei

doi:10.1109/tvt.2022.3159271

Cited by 17 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, the CFSFDP algorithm was applied to classify lateral stability. The final simulation results validated the advantages of the proposed method and coordinated control strategy [8]. Ren W et al proposed an improved algorithm to address the limitations of the original CFSFDP algorithm in anomaly detection.…”

Section: Related Workmentioning

confidence: 59%

Semantic Information Classification of IoT Perception Data Based on Density Peak Fast Search Clustering Algorithm

Chen,

Hu,

Wang

2024

IJACSA

View full text Add to dashboard Cite

In the rapidly developing field of the Internet of Things today, effective processing and analysis of perceptual data has become crucial. The perception data of the Internet of Things is usually large, diverse, and presents high-dimensional characteristics, which poses new challenges to data clustering algorithms. This study utilizes the K-center point algorithm to optimize the density peak fast search clustering algorithm, proposes a new clustering algorithm, and applies it to the research of semantic classification of perception data in the Internet of Things. Firstly, the K-center algorithm was used to optimize the clustering center optimization process of the density peak fast search clustering algorithm. Then, the optimized algorithm was applied to the automatic semantic classification model. Thus, a new automatic semantic annotation model for IoT aware data has been established. The research results showed that the classification accuracy of the proposed optimization algorithm was as high as 0.98, and the running stability of the automatic semantic annotation model optimized using this algorithm was as high as 0.99, with a running time as low as 1s. In summary, the automatic semantic annotation model built in this study can effectively improve the efficiency and accuracy of semantic classification, thereby providing more accurate and efficient data support for intelligent services.

show abstract

Section: Related Workmentioning

confidence: 59%

Semantic Information Classification of IoT Perception Data Based on Density Peak Fast Search Clustering Algorithm

Chen,

Hu,

Wang

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…For instance, field tests may encounter challenges in managing environmental and traffic conditions, potentially leading to crashes [52]. Furthermore, the data collection process may take a long time and contain inaccuracies [53]. To overcome the challenges of data collection in this field of study, researchers developed several models that integrate vehicle performance for crash analysis and prevention.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach

Wang,

Zhao,

et al. 2024

Sustainability

View full text Add to dashboard Cite

Many northern hemisphere countries have experienced exceptionally heavy snow, blizzards, and cold snaps in recent years, causing considerable public concern about the high crash rate and safety issues in road traffic. This study used the CarSim dynamics simulation to recreate several vehicle driving scenarios in snow and ice conditions. To explore the influence of speed, curve radius, and road adhesion coefficient on vehicle sideslip and rollover, four lateral stability evaluation indicators, namely lateral offset, lateral acceleration, yaw rate, and roll angle, are chosen. Unfavorable combinations of these factors result in vehicle deviation from their intended trajectory and dramatically increase the likelihood of sideslip and rollover incidents. In particular, road adhesion coefficients ranging from 0.10 to 0.20 lead first to sideslip, while coefficients of 0.21 to 0.35 lead straight to rollover. Additionally, in the initial segment of the curve, cars are more susceptible to lateral instability. Curve radius has the greatest influence on sideslip when the three influencing factors are combined, while speed is the key component affecting rollover incidents. Smaller curve radii and higher speeds are major factors in such incidents. The results are helpful for proper road alignment parameter selection and dynamic speed-limit measures. This can provide a theoretical basis for traffic management departments to take targeted measures, which is of great significance to improving road traffic safety in snowy and icy weather.

show abstract

“…For in-wheel motor drive electric vehicles, the main problems of DYC arise from how to calculate the optimal yaw moment and generate the desired moment by allocating the torque of each wheel. Some systems integrate active front steering and DYC to realize stability control [7]. For example, Wu et al [8] proposed a divisional adaptive coordination model predictive control (MPC) controller by adjusting the weights between the active front steering and DYC according to the stability margin.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Stability Control Based on Sliding Model Control for BEVs Driven by In-Wheel Motors

Guo

Feng

et al. 2023

Sustainability

View full text Add to dashboard Cite

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control.

show abstract

Vehicle Lateral Stability Control Based on Stability Category Recognition With Improved Brain Emotional Learning Network

Cited by 17 publications

References 15 publications

Semantic Information Classification of IoT Perception Data Based on Density Peak Fast Search Clustering Algorithm

Semantic Information Classification of IoT Perception Data Based on Density Peak Fast Search Clustering Algorithm

Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach

Adaptive Stability Control Based on Sliding Model Control for BEVs Driven by In-Wheel Motors

Contact Info

Product

Resources

About