A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics

Zha, Yunfei; Deng, Jiewei; Qiu, Yinyuan; Zhang, Kun; Wang, Yanyan

doi:10.4271/10-07-02-0014

Cited by 46 publications

(4 citation statements)

References 146 publications

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“…The authors established a vertical dynamic model to enhance passenger comfort in high-speed trains caused by vibrations in the overall structure [10]. Low-frequency resonance was reduced by building a suspension system based on a damper.…”

Section: Factors Affecting Rtcs On Freewaysmentioning

confidence: 99%

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Almahdi,

Al Mamlook,

Bandara

et al. 2023

Sustainability

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Freeway crashes represent a significant and persistent threat to road safety, resulting in both loss of life and extensive property damage. Effectively addressing this critical issue requires a comprehensive understanding of the factors contributing to these incidents and the ability to accurately predict crash severity under different traffic conditions. This study aims to improve the accuracy of crash classification by incorporating key traffic-related variables such as braking, weather conditions, and speed. To validate the effectiveness of proposed model, we utilize real-world crash data from Flint, Michigan. To achieve the objective, we employ an innovative Boosting Ensemble Learning approach, leveraging five advanced ensemble learning models: Gradient Boosting, Cat Boost, XGBoost, LightGBM, and SGD. Through the application of hyperparameter optimization techniques, we further enhance the performance of these models, improving their predictive capabilities. Our evaluation results demonstrated the effectiveness of our approach, with Gradient Boosting algorithms achieving an accuracy rate of up to 96% in crash classification. This research provides valuable insights into the potential of using Boosting Ensemble Learning as a tool for accurately and efficiently classifying freeway crashes across a spectrum of traffic conditions. Additionally, it sheds light on the nuanced variations in crash mechanisms observed when employing diverse ensemble learning models. The findings of this study underscore the significance of hyperparameter optimization as a critical factor in elevating the predictive precision of freeway crashes.

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Section: Factors Affecting Rtcs On Freewaysmentioning

confidence: 99%

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Almahdi,

Al Mamlook,

Bandara

et al. 2023

Sustainability

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show abstract

“…In the context of Industry 4.0, automation technology is undergoing substantial growth (Wang et al, 2023;Zha et al, 2023;Zhang et al, 2023). Industrial robots, as a fundamental component of this paradigm, are widely employed across diverse manufacturing fields (Long et al, 2021;Wang, Lee, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

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“…Geometric-based control methods include pure pursuit control [2] and the Stanley tracking algorithm [3]. Lal et al [4] designed a lateral control method for intelligent vehicles based on the pure pursuit algorithm, which was validated across various curvatures for path tracking problems.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

Cai,

Qu,

Wang

et al. 2024

WEVJ

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To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

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A Survey of Intelligent Driving Vehicle Trajectory Tracking Based on Vehicle Dynamics

Cited by 46 publications

References 146 publications

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

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