XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Zhao, Chen; Zhao, Xia; Zhao, Li; Zhang, Qiong

doi:10.3390/su14116829

Cited by 3 publications

(4 citation statements)

References 46 publications

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“…The XGBoost algorithm is an integrated learning method, which is widely used in all walks of life, and is also deeply researched and developed by different professionals. C Zhao et al established a prediction model of speed estimation and distance between other vehicles during vehicle operation by www.ijacsa.thesai.org mixing neural network and limit gradient, and found that the model can help drivers effectively predict the speed and distance of other vehicles when changing lanes in different scenarios, with good prediction effect [9]. W niu et al integrated a gradient lifting algorithm into computer traffic monitoring to protect computer security and monitor hacker attacks.…”

Section: Related Workmentioning

confidence: 99%

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Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Wang¹

2023

IJACSA

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Preventing financial fraud in listed companies is conducive to improving the healthy development of China's accounting industry and the securities market, is conducive to promoting the improvement of the internal control system of China's enterprises, and is conducive to promoting stability. Based on the combination of SHAP (Shapley Additive explanation), a prediction and identification model should be built to determine the possibility of financial fraud and the risk of fraud for the company. The research model has effectively improved the identification accuracy of financial fraud in listed companies, and the research model has effectively dealt with the gray sample problem that is common in the forecasting model through the LOF algorithm and the IF algorithm. When conducting comparative experiments on the models, the overall accuracy rate of the research model is over 85%, the recall rate is 78.5%, the precision rate is 42%, the AUC reaches 0.896, the discrimination degree KS reaches 0.652, and the model stability PSI is 0.088, compared with traditional financial fraud Forecasting models FS model and CS model has a higher predictive effect. In the empirical analysis, selecting a company's fraud cases in 2020 can effectively analyze the characteristic contribution in the fraud process and the focus on fraud risks. The established model can effectively monitor the company's finance and prevent fraud.

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Section: Related Workmentioning

confidence: 99%

“…In Binford's hypothesis test, the chi-square test is selected to test the degree of conformity of the numerical distribution of the observed samples. The calculation formula of statistics is shown in formula (9).…”

Section: Ln( )mentioning

confidence: 99%

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Wang¹

2023

IJACSA

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“…In the context of rapid lane changes, research has predominantly utilized image-based algorithms to detect lane changes [5][6][7][8][9], often incorporating information about surrounding vehicles [3,[10][11][12][13]. J.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While traditional machine learning techniques can be used to detect lane change, it remains challenging to equip individual two-wheeled vehicles with devices capable of detecting information about surrounding vehicles. On the other hand, C. Zhao et al (2022) estimated lane change using data on relative distances and driving speeds of adjacent vehicles [12]. They compared various machine learning models and a multilayer perceptron in terms of architectural complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches

Kim,

Jun

2023

Applied Sciences

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The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future.

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Regularities of the traffic lane change by the driver when interacting with car-obstacle

Прасоленко¹,

Chumachenko²

2023

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The paper presents the results of experimental studies of drivers` behavior when interacting with obstacles caused by parked vehicles. Today, parking cars on two-lane streets is a significant problem for drivers while driving as it creates obstacles. Drivers need to spot a parked car in time and perform a lane change maneuver. It affects the trajectories of vehicles and the functional state of the driver. The driver needs a certain amount of time to maneuver, which consists of the reaction time, the decision to change the lane, and the execution of the action. It complicates traffic conditions for the driver and creates danger for driving. If the driver does not receive information about the parking location on the street with high-speed traffic in time, the probability of danger increases significantly. In addition, drivers try to change the traffic lane, which is further occupied by parking, in advance to reduce the impact of parking on the functional state of their bodies. There is also a deviation in the cross-section of the street when the speed of movement increases relative to the parked car, which finally indicates a change in the position in the traffic lane. It was established that drivers individually choose the trajectories of changing the traffic lane by the speed of movement. In addition, each driver subjectively decides to start changing the traffic lane at his discretion when an obstacle occurs at a certain distance. Angular velocity was used as an indicator of the probability of finding an obstacle object in a dangerous state. Angular speed is the main parameter in the orientation of the driver and signals the danger. When the angular velocity was 0.015-0.03 rad/c, drivers tried to complete the maneuver and leave a certain distance from the obstacle (safety gap). It indicates some interval of angular velocity in relation to the perception of an obstacle object in space and the sense of danger. The resulting patterns of changing lanes by drivers allow for determining the safe distance to parking and ensuring traffic safety by using appropriate markings and road signs

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XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Cited by 3 publications

References 46 publications

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

Research on the Model of Preventing Corporate Financial Fraud under the Combination of Deep Learning and SHAP

The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches

Regularities of the traffic lane change by the driver when interacting with car-obstacle

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