Predicting Vehicle Behavior Using Multi-task Ensemble Learning

Khoshkangini, Reza; Mashhadi, Peyman Sheikholharam; Tegnered, Daniel; Lundström, Jens; Rögnvaldsson, Thorsteinn

doi:10.1016/j.eswa.2022.118716

Cited by 13 publications

(3 citation statements)

References 46 publications

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“…The method highly supports the network to increase the predictive model’s performance compared to the individual model by decreasing the generalization error. Since training multiple deep networks is computationally expensive, we utilized Snapshot Ensemble to train multiple models by a single training process [ 71 ]. We utilized this approach, which we introduced in [ 71 ], and adapted and integrated it into the optimization technique for the breakdown prediction.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Since training multiple deep networks is computationally expensive, we utilized Snapshot Ensemble to train multiple models by a single training process [ 71 ]. We utilized this approach, which we introduced in [ 71 ], and adapted and integrated it into the optimization technique for the breakdown prediction. However, this technique has the challenge of generating similar models, resulting in similar prediction performances.…”

Section: Proposed Approachmentioning

confidence: 99%

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A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Khoshkangini

Tajgardan

Lundström

et al. 2023

Sensors

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Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Khoshkangini

Tajgardan

Lundström

et al. 2023

Sensors

Self Cite

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“…This study employed the Sum Rule classifier and achieved an F1-score of 90.02% [86]. Another study developed an ensemble learning method for vehicle behavior prediction, where both single-task and multi-task approaches generally outperformed others, with accuracies ranging from 0.72 to 0.94 [87]. Similarly, an urban driving perception method was developed using highresolution maps and data-driven models, which enhanced maneuver prediction accuracy by up to 56% compared to a comparative approach [88].…”

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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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