Short-Term Lateral Behavior Reasoning for Target Vehicles Considering Driver Preview Characteristic

Zhou, Zhisong; Liu, Ronghui; Wei, Chongfeng; Du, Haiping; Yin, Chengliang

doi:10.1109/tits.2021.3107310

Cited by 8 publications

(2 citation statements)

References 24 publications

(43 reference statements)

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“…Afterward, it creates another generation solution via duplication, crossover, and mutation, which minimizes the fitness value. Generally, the GA is used for attribute extraction [55,56]. The GA is also used to concurrently find various spot data.…”

Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

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Section: Multi-objective Genetic Algorithmmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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show abstract

“…A large number of studies have shown that lane-changing behavior of drivers will affect the surrounding vehicles and thus increase the occurrence of accidents. Experts in the field of intelligent transportation in Germany have called for more research into lane-changing safety in order to reduce traffic safety problems caused by lane changing [ 7 ]. The vast majority of studies mainly focus on how to unlock the turning lane change behavior, but in the actual process, due to the low-signal stimulation rate, there is delay, mainly manifested as the lane change after the driver turns on the turn signal [ 8 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

Feng

Yan

2022

Computational Intelligence and Neuroscience

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With the development of technology, vehicle trajectory prediction and safety decision technology has become an important part of active safety technology. Among them, the vehicle trajectory prediction technology can predict the vehicle position, speed, and other motion states in the predicted period according to the current and historical vehicle running state, and the prediction results can provide support for judging the vehicle safety in the predicted period. In order to analyze the above problems, this study fully extracted the main feature information from the vehicle lane change track data with the help of the powerful nonlinear learning and high pattern recognition ability of support vector machine, and conducted identification modeling for the actual lane change process of the vehicle and predictive analysis of the vehicle lateral movement track. First, the lane-changing behavior of vehicles was analyzed, and the vehicle lane-changing execution stage and 10 influencing factors that could characterize lane-changing behavior were determined based on NGSIM to extract the data of lane-change-related variables. Then, a lane-changing recognition model based on gridsearch-PSO is proposed. In the Matlab environment, the model has a test accuracy of 97.68%, while the SVM model without optimization parameters has a recognition accuracy of only 80.87%. The results show that the model has strong classification ability and robustness. Finally, by using the polynomial model for lateral movement trajectory fitting, K-fold cross-validation method is used for fitting polynomial model fitting test.

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Real-Time Classification of Vehicles Using Machine Learning Algorithm on the Extensive Dataset

Pemila,

Pongiannan,

Narayanamoorthi

et al. 2024

IEEE Access

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Short-Term Lateral Behavior Reasoning for Target Vehicles Considering Driver Preview Characteristic

Cited by 8 publications

References 24 publications

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

Real-Time Classification of Vehicles Using Machine Learning Algorithm on the Extensive Dataset

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