A Human-Like Trajectory Planning Method on a Curve Based on the Driver Preview Mechanism

Zhao, Jian; Song, Dongjian; Zhu, Bing; Sun, Zhuo; Han, Jiayi; Sun, Yuhang

doi:10.1109/tits.2023.3285430

Cited by 33 publications

(6 citation statements)

References 48 publications

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“…OpenStreetMap [165] [166] , [167] , [168] Transportation Networks [169] [170] , [171] , [172] DTAlite [173] [174] , [175] , [176] PeMS [177] [178] , [179] , [180] New York City Taxi Data [181] [182] , [183] , [184] 图 3 描述了道路结构认知领域的表征方式及发展脉络。传统自动驾驶方案主要基于高精地图信息，预测周围车辆未来数秒时间内的驾驶行为和运动轨迹 [133,134] ，进而规划出安全、高效、舒适行驶的自车运动轨迹。近年来在车联网技术加持下，也有侧重于通过优化多车轨迹节省燃油消耗的相关研究 [135,136] 。车辆级别，结合车辆运动学和动力学模型，获取操纵车辆加减速和转向所对应的油门与刹车踏板行程、方向盘或转向轮转角，以最小操纵代价的前提下驱使车辆完成给定的行驶轨迹。表 4 列出了自动驾驶预测与规划任务的数据集。在运动预测方面， Argoverse 运动预测数据集 [16] 12…”

Section: Conservationunclassified

自动驾驶开源数据体系：现状与未来

Li,

Wang

et al. 2024

Sci. Sin.-Inf.

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Section: Conservationunclassified

自动驾驶开源数据体系：现状与未来

Li,

Wang

et al. 2024

Sci. Sin.-Inf.

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“…By quantifying node impurity, the Gini index mathematically describes the DT. The equation for the Gini index is as follows in Equation (18).…”

Section: Decision Tree Classifiermentioning

confidence: 99%

“…Moreover, up to 96% of crashes are attributed, at least partially, to driver mistakes [17]. Consequently, improving human variables that influence dangerous driving behaviors is crucial for creating effective treatments to reduce the likelihood of crashes and increase road safety [18,19].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Alam,

Wang,

Pervez

et al. 2024

Sustainability

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Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety.

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“…Some researchers applied statistical methods [ 18 , 19 ], reinforcement learning approaches [ 20 , 21 ], hybrid models [ 22 , 23 ] and deep learning models [ 24 ]. A deep convolutional neural network and random forest are employed for the accident risk prediction method in [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

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A Human-Like Trajectory Planning Method on a Curve Based on the Driver Preview Mechanism

Cited by 33 publications

References 48 publications

自动驾驶开源数据体系：现状与未来

自动驾驶开源数据体系：现状与未来

Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

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