Frame-rate vehicle detection within the attentional visual area of drivers

Zabihi, Seyed Mohsen; Beauchemin, Steven S.; Medeiros, Edil; Bauer, Michael

doi:10.1109/ivs.2014.6856601

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…2) Evaluation and Limitations: Evaluating driver awareness models is not trivial, however indvidual modules can be evaluated quantitatively. For example, measuring gaze estimation error [176], [177], road user trajectory prediction accuracy [162], object detection [166], [178], etc.. In contrast, there are no unified approaches for evaluating the performance of the entire system.…”

Section: E Driver Awareness Estimationmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers' attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in-vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers' attention, i.e. where and when they look, for the two latter categories of applications.We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers' gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at https://github.com/ykotseruba/attention_and_driving

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Section: E Driver Awareness Estimationmentioning

confidence: 99%

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Kotseruba

Tsotsos

2022

IEEE Trans. Intell. Transport. Syst.

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“…Traffic objects are filtered from the Situation Model so that only objects relevant for the situation remain, since for those a warning strategy is formulated. For the detection of fixations on objects in real driving setups, most models use broad tolerance thresholds for the gaze measurements for increased robustness [25][26][27][28] as remote eye tracking systems in automotive applications often do not reach the necessary precision to rely solely on the measurements themselves [17]. The opening angle of the cone around the gaze direction vector is commonly set to about 10°to 13°which corresponds to the parafovea region on the cornea of the human eye.…”

Section: Driver Visual Cues For Warning Strategy Adaptationmentioning

confidence: 99%

The PRORETA 4 City Assistant System

Schwehr

Luthardt

Dang

et al. 2019

At - Automatisierungstechnik

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The use of machine learning in driver assistance systems allows to significantly enhance their functionalities. In particular, it allows to personalize systems by evaluating the driver’s past behavior. Such personalization is especially relevant for recommendations in maneuvers where the specific maneuver embodiment strongly depends on the driver’s momentary driving style and attention. Led by this idea, PRORETA 4 developed a prototypical City Assistant System, which gives the driver a personalized recommendation in urban scenarios. To adapt the recommendations and warnings appropriately, the system incorporates the learned momentary driving style and the driver’s gaze behavior. In this work, we describe the main functional blocks of the system, present our solutions to major implementation challenges and also discuss the safety of the used learning algorithm.

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