A support vector machine approach to unintentional vehicle lane departure prediction

Albousefi, Alhadi Ali; Ying, Hao; Filev, Dimitar; Syed, Fazal; Prakah-Asante, Kwaku O.; Tseng, Finn; Yang, Hsin-Hsiang

doi:10.1109/ivs.2014.6856602

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…More specifically, we developed a two-stage SVM training scheme that employed a nonlinear binary SVM to classify vehicle variable time series (e.g., lateral position and lateral velocity) to predict occurrence of a lane departure. We reported the preliminary results on one-stage training and testing of the SVM on lane departure prediction (Albousefi et al, 2014). The present paper significantly extends the scope and depth of our research by introducing the two-stage SVM training and exploring longer prediction horizons.…”

Section: Introductionmentioning

confidence: 71%

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Albousefi

Ying

Filev

et al. 2016

Journal of Intelligent Transportation Systems

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Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about three-hour driving per subject) and six control drivers (approximately 20 minutes driving each). A total of 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of over 7.5 million prediction decisions demonstrates that: (1) Downloaded by [La Trobe University] at 03:56 16 June 2016 ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 2 excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), wereachieved when the prediction horizon was 0.6 s or less, (2) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (3) the radial basis function performed the best as the SVM kernel function.

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Section: Introductionmentioning

confidence: 71%

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Albousefi

Ying

Filev

et al. 2016

Journal of Intelligent Transportation Systems

Self Cite

View full text Add to dashboard Cite

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“…Several research works, e.g., [115] and [116], implemented an SVM algorithm as the classifier for lane departure warning (LDW), while the authors of [117] opted for DNN for the application. In [115], SVM-based prediction of the trajectory of lane-changing vehicles is performed using actual Next Generation Simulation (NGSIM) field data.…”

Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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“…For example, lane departures are considered indicators of car crashes 9 . Traditionally, the binary outcome and count of lane departures are used to measure unsafe driving 13,14 and can be evaluated with the zero‐inflated count models. However, it is also important to understand how far off a driver may drift off their intended lane 15 .…”

Section: Introductionmentioning

confidence: 99%

Sparse group regularization for semi‐continuous transportation data

Feng

Boyle

2021

Statistics in Medicine

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Motor vehicle crashes are a global public health concern. Most analysis have used zero‐inflated count models for examining crash counts. However, few methods are available to account for safety metrics that have semi‐continuous observations. This article considers the problem of variable selection for the semi‐continuous zero‐inflated (SCZI) models. These models include two parts: a zero‐inflated part and a nonzero continuous part. A special group regularization is designed to accommodate the unique structure of two‐part SCZI models, and a type of Bayesian information criterion is proposed to select tuning parameters. We illustrate the variable selection process of the proposed model using lane position data from a driving simulator study. In the study, drivers stay in the intended lane for the majority of their drive (zero‐inflated part). On occasion, some drivers do drift out of their intended driving lane (nonzero continuous part). Our findings show that individual differences can be captured with the proposed model, which has implications for driving safety and the design of in‐vehicle alerting systems.

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A support vector machine approach to unintentional vehicle lane departure prediction

Cited by 11 publications

References 16 publications

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Sparse group regularization for semi‐continuous transportation data

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