Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

Reicherts, Sebastian; Hesse, Benjamin Stephan; Schramm, Dieter

doi:10.1109/ojits.2021.3093712

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The former requires a vehicle model. This model is often non-linear [9] [10] [11] [12] and its parameters are not always easy to identify [13] [14]. Vehicle models can be divided into two categories: kinematic and dynamic.…”

Section: Introductionmentioning

confidence: 99%

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

Marotta,

Strano,

Terzo

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle's sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.

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Section: Introductionmentioning

confidence: 99%

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

Marotta,

Strano,

Terzo

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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“…Albers et al [2] showed the prospective of automotive development processes driven by sensor data. More specifically, Reicherts et al [3] used naturalistic driving studies to identify vehicle dynamics. Tanshi and Söffker [4] proposed a method for determining the takeover time budget based on the analysis of driver behavior.…”

Section: Introductionmentioning

confidence: 99%

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

Ling,

Thiele,

Setzer

2024

IEEE Open J. Intell. Transp. Syst.

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We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarsergrained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss. Although information loss with each step is well understood, little guidance exists on the overall information loss when conducting both steps sequentially. We use Monte Carlo simulations to regress information loss on the number of bins and PCs, given few parameters of a dataset related to its scale and correlation structure. A sensitivity study shows that information loss can be approximated well given sufficiently large datasets. Using the number of bins, PCs, and two correlation measures, we derive an empirical loss model with high accuracy. Furthermore, we demonstrate the benefits of estimating information losses and the representativeness of total loss in evaluating the accuracy of k-means clustering for a real-world customer fleet dataset. For preprocessing sensor data which are aggregated from sufficient number of samples, continuously distributed, and can be represented by Beta-distributions, we recommend not to coarsen the histogram binning before PCA.

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