Predicting driver’s lane-changing decisions using a neural network model

Zheng, Jian; Suzuki, Koji; Fujita, Motohiro

doi:10.1016/j.simpat.2013.12.007

Cited by 89 publications

(41 citation statements)

References 31 publications

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“…In recent years, the great popularity of artificial intelligence has led to some lane-changing behavior studies based on it. e neural network model can predict lane-changing behavior more accurately than the multinomial logit model [25]. It was adopted to explore the relationship between the driver's lane-changing intention and lane-changing acceptance [26] and to analyze traveling heading angle as well as acceleration during lane-changing [27].…”

Section: Lane-changing Behaviormentioning

confidence: 99%

Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

Long

Zhang

Liu

et al. 2020

Journal of Advanced Transportation

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In this paper, the decision-making model of discretionary lane-changing is established using cumulative prospect theory (CPT). Through analyzing the vehicles’ dynamic running states, safety spacing calculating approaches for discretionary lane-changing and lane-keeping have been put forward firstly. Then, based on CPT, a lane-changing decision model with accelerating space as its utility is proposed by estimating the difference between actual spacings and the safety spacings for discretionary lane-changing as well as lane-keeping. In order to calculate the utility of discretionary lane-changing, dynamic reference points and a parameter representing driver’s risk preference are introduced into the model. With the real data collected from an urban expressway, the distribution of discretionary lane-changing duration is analyzed, and the model parameters are also calibrated. Furthermore, the applicability of the model is evaluated by comparing with the actual observation and random unity model. Finally, the sensitivity analysis of the model is carried out, that is, assessing the influence degree of each variable on the decision result. The study reveals that the CPT-based model can describe discretionary lane-changing behavior more accurately, which consider drivers’ risk-aversion during decision-making.

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Section: Lane-changing Behaviormentioning

confidence: 99%

Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

Long

Zhang

Liu

et al. 2020

Journal of Advanced Transportation

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“…[27][28][29][30] Some microscopic lanechanging models use classifiers or neural networks to predict lane-changing events. [31][32][33][34] Although these models perform well for resolving complex problems, the specific effects of individual contributing factors are difficult to address in detail.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring factors contributing to lane changes during left turns on quadruple left-turn lanes at signalized intersections

Fan

Wang

Lü

2017

Advances in Mechanical Engineering

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Quadruple left-turn lanes are employed gradually at some major intersections in Jilin, China. At these intersections, frequent lane changes during left turns of vehicles can lead to vastly disordered traffic operations. In this study, the factors contributing to lane changes during left turn behaviors at quadruple left-turn lane intersections were interpreted based on a field survey. In total, data on 192 green intervals and 550 individual lane-change behaviors were collected in Changchun, Jilin Province. A lane-changing rate model was established to predict the rate of lane changes during left turns. In addition, an interval selection model was built to estimate the probability of lane changes during left turn (from inside to outside only) for an individual vehicle using logistic regression. The two models provide insights to assess the traffic conditions of quadruple left-turn lane at intersections. The results of this study suggest that weaving distance, initial lane, traffic flow rate, and percentage of large vehicles significantly contribute to lane changes during left turn. Based on the identified factors, safety countermeasures to reduce lane changes during left turn behaviors are proposed.

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“…Some previous studies indicate that lane changing and lane merge maneuvers account for approximately 5% of all crashes and as high as 7% of all crash fatalities (Chovan, Tijerina, Alexander, & Hendricks, 1994;Habenicht, Winner, Bone, Sasse, & Korzenietz, 2011;Rodemerk, Habenicht, Weitzel, Winner, & Schmitt, 2012). Due to the importance of the lane changing decision, recent literature focus on various aspects of lane changing such as discretionary lane-change characteristics multiple-vehicle collisions (Nagatani & Yonekura, 2014), predicting driver lane changing behavior (Zheng, Nagoya, & Suzuki, 2014), modeling highway lane changing , detecting lane change cut-ins using video images (Lee, Kim, Lee, Lee, & Kim, 2013), lane changing on curved roads (Guo, Ge, Yue, & Zhao, 2014), lane changing and heavy vehicles (Yang, Wang, & Wang, 2014), lane changing rules for microscopic modeling (Hable & Schreckenberg, 2014), and lane changing trajectory tracking (Ren, Zhang, & Wang, 2014).…”

Section: Problem and Motivationmentioning

confidence: 99%

Situation assessment and decision making for lane change assistance using ensemble learning methods

Hou

Edara

Sun

2015

Expert Systems with Applications

116

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Predicting driver’s lane-changing decisions using a neural network model

Cited by 89 publications

References 31 publications

Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

Exploring factors contributing to lane changes during left turns on quadruple left-turn lanes at signalized intersections

Situation assessment and decision making for lane change assistance using ensemble learning methods

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