A statistical learning approach for estimating the reliability of crash severity predictions

Müller, Marcus G.; Nadarajan, Parthasarathy; Botsch, Michael; Utschick, Wolfgang; Bohmlander, Dennis; Stefan, Katzenbogen

doi:10.1109/itsc.2016.7795911

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Before the implementation, the number of trees (classifiers) and the number of randomly sampled features at each node should be determined. Some studies employ this method and find it outperforms other ML algorithms [9], [47], [48]. However, its impurity variable importance may be biased towards variables with more classifications or continuous values.…”

Section: Ensemble Modelsmentioning

confidence: 99%

Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Levinson

2020

IEEE Open J. Intell. Transp. Syst.

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Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanismrelated variables using ensemble machine learning models. The results demonstrate selected models can predict severity at a high level of accuracy. The stacking model with a linear blender is preferred for the designed ensemble combination. Most bagging, boosting, and stacking algorithms perform well, indicating ensemble models are capable of improving upon individual models.

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Section: Ensemble Modelsmentioning

confidence: 99%

Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Levinson

2020

IEEE Open J. Intell. Transp. Syst.

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“…A detailed overview of typical patterns can be found in the survey [23]. In this work the proximity matrix is shown as a square image, where dark blue pixels represent zero entries (P ij = 0) and yellow pixels one entries 3 . One can think about bright squares along the diagonal representing clusters.…”

Section: Matrix Reorderingmentioning

confidence: 99%

“…The input data can be obtained from vehicle sensors, as well as from other sources, like navigation data or information from vehicle2Xcommunication. The method can also be applied in other research topics like crash severity predictions [3]. Further thoughts on other applications will follow in Section VI.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

Kruber

Wurst

Botsch

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper.The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work.

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“…With an increasing number of vehicles on the road, there is a rising interest and necessity of analyzing and planning trajectories in complex multi-modal traffic scenarios. One goal for trajectory planning is the implementation of vehicle active safety systems for collision avoidance and mitigation [2]. Globally, there are more than 1.2 million traffic-accident related fatalities per year [3].…”

Section: Introductionmentioning

confidence: 99%

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Morales

Membarth

Gaull

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multiobject traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles.We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX 2.

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A statistical learning approach for estimating the reliability of crash severity predictions

Cited by 9 publications

References 16 publications

Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

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