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A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A 12-feature SVM plus learned subsequence codebooks can detect road anomalies, size potholes, and classify driving style from a phone's accelerometer, outperforming threshold heuristics and hand-crafted descriptors.

desk verdict Useful detector comparison and a novel learning-to-rank framing, but the headline pothole-depth numbers are compromised by likely cross-validation leakage. read the letter →

arxiv 1908.10187 v1 pith:MTUKZRE7 submitted 2019-08-15 cs.LG eess.SPstat.ML

M. Ricardo Carlos This is my paper · ORCID
classification cs.LGeess.SPstat.ML
keywords smartphonesensingaccelerometerroadanomalydetectionpotholedepthestimationaggressivedrivingclassificationbag-of-wordslearningtorankmachine
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This dissertation tries to establish that machine-learning analysis of smartphone accelerometer time series, rather than threshold heuristics or hand-built descriptors, is the right toolkit for assessing road condition and driving style from opportunistic sensing. On a shared test of thirty virtual roads, a 12-feature support-vector machine attains the highest average F1 among all compared road-anomaly detectors (0.785), statistically comparable to a tuned standard-deviation threshold and clearly better than the most-cited threshold schemes. For pothole characterization, a random forest regresses depth from a 52-feature time- and frequency-domain vector with 22% average relative error, and the error is essentially uncorrelated with vehicle speed ($r = -0.0132$). For aggressive driving, the bag-of-words representation built from clustered acceleration subsequences outperforms hand-crafted statistical descriptors by more than five percentage points in accuracy, F-measure, and G-means on two datasets. The dissertation also shows potholes can be ranked by depth (Kendall's $\tau$ of 0.34) and that all tasks can be combined in one routing pipeline with 0.94 F-score at the routing stage.

What carries the argument

The load-bearing mechanism is the replacement of hand-set thresholds and hand-crafted descriptors by learned decision functions: a 12-feature SVM for anomaly detection, a 52-feature regression and ranking vector for pothole depth, and a bag-of-words codebook for maneuver classification. In the bag-of-words model, training signals are cut into length-$L$ windows, clustered into $K$ centroids, and any new signal is encoded as a histogram of nearest-centroid counts; the codebook of characteristic subsequences is the central object that carries the aggressive-driving argument. For pothole depth, the 52-feature vector summarizes vertical acceleration, jerk, and the first two integrals (vertical speed and vertical displacement) with thirteen time- and frequency-domain descriptors each. The virtual-road construction, which concatenates trimmed anomaly samples into synthetic long drives, is what permits the first controlled comparison of the threshold detectors under identical conditions.

What would settle it

Run a leave-one-pothole-out cross-validation on the 857 pothole samples: train on all passes over other potholes and test on every pass over one held-out pothole; if the average relative error climbs well above 22% or the speed correlation departs from $r = -0.0132$, the depth-estimation generalization claim as stated is not supported.

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Extended reading notes

Core claim

The central discovery is that learned representations solve the tasks where threshold heuristics and hand-crafted features do not transfer. On road-anomaly detection, the dissertation's 12-feature SVM, which combines statistical window descriptors with threshold-derived confidence scores, reaches an average F1 of 0.785 across 30 virtual roads, above the standard-deviation threshold detector (0.769) and far above Z-THRESH (0.706), Z-DIFF (0.528), Nericell (0.675), and Pothole Patrol (0.450). Pothole depth is estimated by random-forest regression on 52 features derived from vertical acceleration, its derivative, and its first two integrals, giving 22% relative error, and shallow-versus-deep classification reaches 0.89 AUC; a learning-to-rank variant orders potholes by depth with Kendall's $\tau$ of 0.34. Aggressive-driving maneuvers are classified from bag-of-words histograms of learned subsequence shapes, which beat the statistical summarization baseline by more than 5% in accuracy, F-measure, and G-means on both the author's data and Ferreira et al.'s data. The dissertation concludes that a single machine-learning pipeline can route smartphone accelerometer readings into road-anomaly and aggressive-driving branches, with the routing classifier scoring 0.94 F-measure.

Load-bearing premise

The load-bearing premise in the pothole characterization is that the 857 samples collected by repeatedly passing over the same physical potholes in eight cars can be treated as independent examples, because the 10-fold cross-validation is not grouped by pothole, car, or session; if passes over the same pothole appear in both training and test folds, the reported 22% relative error overstates accuracy on potholes never seen before.

Editorial extensions

If this is right

  • If the 12-feature SVM generalizes beyond the virtual-road test, road-anomaly detection can shed manual threshold tuning: a single learned detector replaces several cited heuristics with a better average F1.
  • If pothole depth estimation is truly speed-independent, mobile surveys can be pooled across vehicles and speeds without per-speed calibration, making crowdsourced depth maps feasible.
  • If bag-of-words retains its margin on larger populations, aggressive-driving telematics can rely on automatically learned maneuver shapes instead of engineered features, easing transfer across devices and drivers.
  • A positive result for pothole ranking means maintenance crews could prioritize the most severe anomalies from phone data alone, not just count them.
  • The unified pipeline shows road-quality and driving-style monitoring can share one sensing front-end, so a single app can feed both maintenance and safety services.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the reported speed-independence of pothole depth error suggests a global model could be trained on mixed-speed fleet data and deployed without per-vehicle or per-speed calibration; this is testable by binning errors by speed on a held-out city-scale collection.
  • Beyond the paper, the same codebook-and-ranking machinery could be transferred to other IMU event-recognition problems (falls, rail crossings, door slams) where the event shapes are unknown in advance, since the method learns its own vocabulary of shapes.
  • Beyond the paper, the finding that vertical reorientation is reliable but triaxial yaw estimation carries errors above 20 degrees implies that the road-anomaly branch of the pipeline is more deployment-ready than the aggressive-driving branch, which depends on lateral and longitudinal axes; a field test with freely placed phones would bound that gap.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This dissertation presents machine-learning methods for road anomaly detection, pothole depth characterization, and aggressive driving classification from smartphone accelerometer data. Chapter 3 evaluates sensor-reorientation strategies. Chapter 4 compares influential threshold-based detectors and proposes a 12-feature SVM detector and an ensemble, reporting the SVM with the highest average F1 (0.785) on 30 synthetic "virtual roads." Chapter 5 proposes regression, classification, and ranking for pothole depth and speed-bump condition, reporting 22% relative error, 0.89 AUC, and Kendall tau 0.34. Chapter 6 uses bag-of-words and related representations for aggressive driving, reporting improvements of over 5% over hand-crafted descriptors on two datasets. Chapter 7 combines these components into a pipeline with a routing classifier. The evaluation relies on self-collected data, with code and data made public.

Significance. If the quantitative claims survive scrutiny, the dissertation makes a useful contribution: it directly compares influential detectors on common data, shows that learned representations can replace threshold heuristics, and introduces pothole-depth ranking. The peer-reviewed provenance of Chapters 3, 4, and 6, the use of Friedman/Nemenyi and Kruskal-Wallis tests, and the honest reporting of a negative result (the ensemble does not beat STDEV) are strengths. The main caveat is that the headline numbers for pothole characterization and the Chapter 4 comparison depend on cross-validation and synthetic-road constructions whose independence properties are not established; the significance of the results is therefore conditional on those being resolved.

major comments (4)
  1. [Section 5.3.1 and 5.3.4, Tables 5.4-5.7] The 10-fold cross-validation is described as a split over the 857 pothole samples, while the dataset was built by passing over the same 163 pothole events several times with different speeds and angles. Because folds are not grouped by pothole or car, repeated passes of the same physical pothole can appear in both training and test folds. A random forest can memorize the acceleration profile of a specific pothole, so the reported 22% relative error, 0.89 AUC, and Kendall tau 0.34 likely overstate generalization to unseen potholes. Please re-run the evaluation with grouped (blocked) folds by pothole event, car, and session, and report the resulting metrics; this is load-bearing for the central claims of Chapter 5.
  2. [Section 4.3.1 and Table 4.2] The virtual-road test set is generated by randomly concatenating a fixed pool of 237 event samples. Testing roads include up to 100 anomalies of a given type or 100 mixed anomalies, which is more than the number of unique potholes (56) or bumps in the pool; the text does not state that training and testing virtual roads use disjoint anomaly samples. If the same event snippets are reused across the split, the reported F1 comparison is optimistic and the claim of a fair head-to-head comparison is compromised. Please specify how the 30 training and 30 testing roads were sampled and ensure, or explicitly demonstrate, that no event snippet appears in both training and testing roads.
  3. [Section 4.3.1] The construction of virtual roads by stitching trimmed one-to-four-second anomaly samples into long series assumes that threshold detectors experience the same statistics as in continuous driving. The manuscript does not validate this assumption, and stitching discontinuities could systematically affect threshold-based algorithms. Please either validate the virtual-road model against at least one continuous recording or state this limitation explicitly and soften the corresponding comparative claims.
  4. [Section 5.4.1] The speed-independence claim is based on a Pearson correlation of r = -0.0132 between speed and error, but the speed description is internally inconsistent: Section 5.3.1 states speeds between 20 and 40 km/h, while Section 5.4.1 mentions samples collected at up to 45 km/h and an average speed of 12.5 km/h. Please reconcile these numbers and re-examine the speed-independence claim within the grouped cross-validation recommended above, since sample-level leakage could also affect this correlation.
minor comments (4)
  1. [Section 2.4.4, Eq. (2.5)] The denominator of accuracy is written as TP+FN+FP+FN; the last FN should be TN.
  2. [Section 2.4.4, Eq. (2.11)] The expression given for AUC is actually the average of sensitivity and specificity, not the area under the ROC curve; please use a correct definition or clarify the intended quantity.
  3. [Chapter 7, Section 7.4] The pipeline evaluation reports F-score values without a statistical comparison or class-wise breakdown; please specify whether the averages are macro or micro and report variability across the 30 runs.
  4. [Section 6.4.1] The statement that using only the lateral axis is the second best alternative should specify whether this refers to accuracy, F-measure, or G-means, since the box plots show different distributions across metrics.

Circularity Check

1 steps flagged · score 3.0 of 10

Partial circularity: one aggressive-driving dataset is reoriented using the known maneuver label, but the main BoW result is independently reproduced on the Ferreira et al. dataset.

  1. self definitional [Section 6.3.1, "Our Data Set" (aggressive driving data collection)]
    "Smartphone orientation was not known at the time of data collection, and were semi-automatically reoriented by performing vertical reorientation by using the methods described in Carlos et al. (2016), using principal component analysis to separate acceleration in the X and Y axes, and then manually inspecting and rotating these last two axes to match the expected behavior for the type of event registered at the moment the acceleration was collected; this method is similar to the one outlined in Larsdotter & Jaller (2014)."

    The reoriented acceleration signals are the inputs to the bag-of-words feature extractor, and the maneuver type is the classification target. The preprocessing step uses the known label ("the type of event registered at the moment the acceleration was collected") to decide how to rotate the X/Y axes, so the feature representation is partly constructed from the target it is later asked to predict. The high scores on this data set (e.g., 0.9688 accuracy in Table 6.3) therefore do not provide fully independent evidence that BoW outperforms hand-crafted descriptors on this collection: part of the signal-to-label association is baked in during reorientation. This is a dataset-specific, partial circularity; the claim is rescued by the Ferreira et al.

full rationale

No load-bearing circularity was found in the main derivation chains of Chapters 4, 5, or 7. The road-anomaly detection comparison (Chapter 4) confronts published threshold detectors and a learned SVM on held-out virtual roads; the SVM feature vector borrows threshold-inspired statistics, but the classifier is trained on labels and evaluated against external baselines, so its F1 scores do not reduce by construction to its inputs. The pothole characterization results (Chapter 5) are anchored to externally measured pothole depths and physical ground-truth labels, and the regression/ranking outputs are not defined in terms of the targets. One validity risk there is that the 10-fold split is described over 857 samples rather than over the 163 pothole events, so repeated passes of the same pothole may appear in both training and test folds; this could inflate the reported 22% relative error and 0.89 AUC, but it is a data-leakage concern, not an exhibited circular equivalence. The aggressive-driving chapter (Chapter 6) contains the one genuine partial circularity identified above: the authors' own data set is reoriented using the known event type before classification. That step compromises the absolute numbers reported on that data set. However, the dissertation's central claim that BoW outperforms hand-crafted descriptors is also demonstrated on the independently collected and pre-oriented Ferreira et al. data set (Table 6.4), so the chapter's core conclusion does not stand or fall on the contaminated data alone. Self-citations to Carlos et al. (2016, 2018) and Gonzalez et al. (2017) are used as normal methodological provenance and as modules in the Chapter 7 pipeline; they are not invoked as an external uniqueness theorem or as a substitute for evaluation. Overall, the dissertation's main results are self-contained against external benchmarks, with one partial, dataset-specific circular step.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The claims rest on four domain assumptions that are stated but not independently validated: gravity-dominated readings for orientation (Eq. 2.3), transferability of anomaly signatures across vehicles and speeds without suspension modeling, subjective driver consensus as ground truth for speed bump condition, and virtual roads as a proxy for continuous driving. The free parameters are hand-set threshold values, retuned baseline thresholds, grid-searched BoW hyperparameters, and model hyperparameters chosen in preliminary experiments. The depth class boundaries are data-dependent. No new physical entities are introduced.

free parameters (6)
  • Threshold values for the SVM feature vector = 0.3g, 0.15g, 0.015g, 0.2g, 3 (Table 4.3)
    Hand-set in Section 4.3.2; the features are threshold-comparison values, so the abstract's claim of removing thresholds is overstated.
  • Retuned thresholds of compared baseline detectors = not reported per detector
    Section 4.3.2 retunes baseline thresholds on the training virtual roads to maximize F1, so the comparison evaluates optimized variants, not the published settings.
  • SVM hyperparameters (C, kernel, window size) = C in {1,10,15,20}; poly, linear, sigmoid, RBF; windows 10-100
    Grid search on the training virtual roads (Table 4.4).
  • Bag of words hyperparameters K and L = K in 2-250, L in 5-300, overlap on/off, TF-IDF on/off
    Grid searched in Section 6.4.1; best configurations are reported against the same data with no described validation split.
  • Characterization model hyperparameters = RF 150 trees, GB 150 trees and 3 nodes, kNN k=5
    Chosen from the best performing configurations in preliminary experiments (Section 5.3.3, Table 5.3).
  • Pothole depth class boundaries = equal divisions of the 4-16 cm range into 2, 3, or 4 classes
    Section 5.4.2; the boundaries are data-dependent and change the classification difficulty.
assumptions (5)
  • domain assumption Roll and pitch are recoverable from gravity via arcsin(a_x/g) and arcsin(a_y/g), Eq. 2.3, assuming gravity dominates the accelerometer reading.
    Used for vertical reorientation in Chapter 3 and for the pipeline features in Chapter 7; degrades during hard acceleration or when the phone is loosely placed.
  • domain assumption Acceleration signatures of road anomalies transfer across vehicles, speeds, and phone placements without modeling suspension dynamics.
    Chapters 4 and 5 train a single model on eight cars and claim speed insensitivity (r = -0.0132, Section 5.4.1); no vehicle-specific parameters are used.
  • domain assumption Driver and copilot consensus is a valid ground truth for speed reducer functional condition.
    Section 5.3.1 labels bumps OK/Not OK by subjective judgment; the resulting AUCs (0.69-0.76) inherit this label noise.
  • ad hoc to paper Stitching trimmed anomaly samples into virtual roads preserves the statistics that threshold detectors see in continuous driving.
    Section 4.3.1 builds synthetic roads for the detector comparison; transitions, traffic, and idle vibration are absent, and the paper notes Pothole Patrol performed poorly in this setup.
  • standard math Euler's rotation theorem guarantees a rotation matrix exists between the device and vehicle frames (Eqs. 2.1-2.2).
    Standard linear algebra underlying all reorientation procedures in Chapter 3.

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Cite this review

Pith. "Pith review of A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style." pith.science (2026). https://pith.science/paper/MTUKZRE7

@misc{pith2026190810187,
  author       = {Pith},
  title        = {Pith review of: A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MTUKZRE7}},
  note         = {Machine review of arXiv:1908.10187}
}
read the original abstract

Road transportation is of critical importance for a nation, having profound effects in the economy, the health and life style of its people. With the growth of cities and populations come bigger demands for mobility and safety, creating new problems and magnifying those of the past. New tools are needed to face the challenge, to keep roads in good conditions, their users safe, and minimize the impact on the environment. This dissertation is concerned with road quality assessment and aggressive driving, two important problems in road transportation, approached in the context of Intelligent Transportation Systems by using Machine Learning techniques to analyze acceleration time series acquired with smartphone-based opportunistic sensing to automatically detect, classify, and characterize events of interest. Two aspects of road quality assessment are addressed: the detection and the characterization of road anomalies. For the first, the most widely cited works in the literature are compared and proposals capable of equal or better performance are presented, removing the reliance on threshold values and reducing the computational cost and dimensionality of previous proposals. For the second, new approaches for the estimation of pothole depth and the functional condition of speed reducers are showed. The new problem of pothole depth ranking is introduced, using a learning-to-rank approach to sort acceleration signals by the depth of the potholes that they reflect. The classification of aggressive driving maneuvers is done with automatic feature extraction, finding characteristically shaped subsequences in the signals as more effective discriminants than conventional descriptors calculated over time windows. Finally, all the previously mentioned tasks are combined to produce a robust road transport evaluation platform.

Figures

Figures reproduced from arXiv: 1908.10187 by the authors.

Figure 2.1
Figure 2.1. Coordinate systems used by the Android API for a) for mobile devices, and b) automotive implementations. Arrows show the positive direction for each axis. Source: Android Open Source Project, accessed 2019-01-19, https://source.android.com/devices/sensors/sensor-types. 2.4 Time Series Fundamentals for ITS 2.4.1 Sensor Reorientation The accelerometers, gyroscopes, and magnetometers found in Android smartphones report… view at source ↗
Figure 2.2
Figure 2.2. Examples of sliding windows with lengths of three elements, with and without overlap￾ping. Centering and scaling are preprocessing techniques used to regularize data sets that contain time series (or windows) with different scales and ranges, with notice￾able outliers or presenting heteroscedasticity. A simple way to perform a correction is by calculating the mean (x¯) and standard deviation (σ) of the elements in t… view at source ↗
Figure 2.3
Figure 2.3. A plot for a time series, along its PAA representation using a word size of 10 and alphabet size of 7. This idea of finding discrete entities is extended by the concept of a shapelet, a subsequence in a time series that is representative of the event of interest (Ye & Keogh, 2009). This concept links a visual idea, a shape that can be seen when plotting data, with a time series reflecting something that is not relat… view at source ↗
Figures from the paper (22 more)
Figure 2.4
Figure 2.4. Figure 2.4: Application of the two stages of a Bag of Words model. In the first stage (fitting): A) a collection of time series is split in segments of length L. B) all segments are considered as points in an L-dimensional space, and a clustering algorithm is used to find K clus…
Figure 3.1
Figure 3.1. Figure 3.1: Trajectory and speed of the vehicle for the third data set, in UTM coordinates. Time Warping (DTW) distance between ground truth and the reoriented versions of the acceleration signals that were captured in unknown orientations. The NCC score goes from -1 to 1, with …
Figure 3.2
Figure 3.2. Figure 3.2: NCC and DTW by method for vertical reorientation. In general, the low DTW distances (less than 0.18) between reoriented signals and ground truth suggests the procedures are effective and that vertical accelera￾tion signals captured with smartphones can be transformed…
Figure 3.3
Figure 3.3. Figure 3.3: A) Reorientation results with NCC ≈ -0.3 and DTW ≈ 0.042. B) Reorientation results with NCC ≈ 0.25 and DTW ≈ 0.16. Raw reoriented data for both examples. It can be concluded from this analysis that the evaluated vertical reorientation proposals, and the usage of the …
Figure 3
Figure 3. Figure 3: shows an example of estimated azimuth and GPS reported bearing. [PITH_FULL_IMAGE:figures/full_fig_p059_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: shows an example of estimated azimuth and GPS reported bearing. Both time series are heavily correlated, but there are sections with noticeable dis￾crepancies. From these results it can be concluded that azimuth estimation produces accept￾able results, but there are …
Figure 3.5
Figure 3.5. Figure 3.5: NCC and DTW by axis for compass-GPS based reorientation. obtained yaw angles to quantify the expected error for the two evaluated methods of reorientation. It was found that many estimations have errors of more than 20º for both meth￾ods. This level of error is signi…
Figure 3.6
Figure 3.6. Figure 3.6: NCC and DTW by axis for accelerometer-GPS based reorientation. many cases with more than 20º of error; the same for the median values of those pre￾dictions. However, the median values of the estimations over the complete length of the time series (about four minutes)…
Figure 4.1
Figure 4.1. Figure 4.1: Examples of vertical acceleration signals: A) Speed bump, B) Metal speed bumps, C) Pothole, D) Normal road [PITH_FULL_IMAGE:figures/full_fig_p068_4_1.png]
Figure 4.2
Figure 4.2. Figure 4.2: A) Sensitivity and precision of every approach for the 30 roads. B) Average performance for all the approaches. detectors for each virtual road in terms of F1. From these plots it can be seen that the detectors behave very differently, with the best performers being …
Figure 4.3
Figure 4.3. Figure 4.3: Rose plots of F1 for the 30 roads for a) G-ZERO, b) STDEV(Z), c) Z-DIFF, d) Z-THRESH, e) Nericell, f)Pothole Patrol, g) Swarm, h) SVM(Z) and i) PERT [PITH_FULL_IMAGE:figures/full_fig_p075_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: CD diagram with statistical comparison of F1 score for all the detectors. 65 [PITH_FULL_IMAGE:figures/full_fig_p075_4_4.png]
Figure 5.1
Figure 5.1. Figure 5.1: Dimensions for all the potholes in the data set. The small insert shows the distribution of depths for all the individual events. the number of samples for each type of anomaly in the data set [PITH_FULL_IMAGE:figures/full_fig_p082_5_1.png]
Figure 5.2
Figure 5.2. Figure 5.2: Average RMSE by real depth for the evaluated algorithms. Feature importance analysis was performed to understand the influence of the different features in the results. Two strategies were applied: first, the function for the split criterion (mean squared error) was …
Figure 5
Figure 5. Figure 5: shows the relative importance for the 52 features, highlighting the [PITH_FULL_IMAGE:figures/full_fig_p086_5.png]
Figure 5.3
Figure 5.3. Figure 5.3: Importance for each feature. The features are represented by their ordinal position, shown in [PITH_FULL_IMAGE:figures/full_fig_p087_5_3.png]
Figure 5.4
Figure 5.4. Figure 5.4: Contrast between the ground truth ordering and the one produced by the point-wise approach. not to separate signals reflecting normal road from those that have an anomaly, but to make a sub-classification of the events, judging them by the level of the vibration indu…
Figure 6.1
Figure 6.1. Figure 6.1: Box plots summarizing the distribution of classification results for the bag of words model. tuned to produce very good results for aggressive driving detection. The box plot in [PITH_FULL_IMAGE:figures/full_fig_p103_6_1.png]
Figure 6.2
Figure 6.2. Figure 6.2: a) CDD for F-measure with respect to K. b) CDD for F-measure with respect to L. 95 [PITH_FULL_IMAGE:figures/full_fig_p105_6_2.png]
Figure 6.3
Figure 6.3. Figure 6.3: Box plots summarizing the distribution of the average results for individual maneuver classification with all the parameters in the grid search for our data set [PITH_FULL_IMAGE:figures/full_fig_p106_6_3.png]
Figure 6
Figure 6. Figure 6: presents the results in terms of accuracy, F-measure, and G-means, for [PITH_FULL_IMAGE:figures/full_fig_p106_6.png]
Figure 7.1
Figure 7.1. Figure 7.1: Diagram of the proposed pipeline. It takes as input acceleration in three dimensions, which might need to be reoriented. Routing is the first step, in which a classifier decides if the acceleration readings reflect normal driving (in which case no further action is r…

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Pith tools

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