Supervised machine learning for modeling human recognition of vehicle-driving situations

Dixon, Kevin R.; Lippitt, C.E.; Forsythe, Jeremy

doi:10.1109/iros.2005.1545026

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…While these are very promising avenues to pursue, we feel that we can offer powerful driver-assistance tools by intelligently analyzing readily available sensors on commercial vehicles to determine how the current situation can impact driver performance. Unsupervised learning has been used as a basis for context recognition for mobile devices [10] and for improving image classification [11] The work presented in this paper extends the previous work in driving-difficulty systems of [12], which trained a classification system to identify potentially dangerous driving conditions using predefined situations. This system identified eight highlevel situations with high accuracy: 1) Approaching or Waiting at Intersection, 2) Leaving Intersection, 3) Entering On-ramp or High-Speed Roadway, 4) Being Overtaken, 5) High Acceleration or Dynamic State of Vehicle, 6) Approaching Slow-Moving Vehicle, 7) Preparing to Change Lanes, and 8) Changing Lanes.…”

Section: Related Workmentioning

confidence: 80%

“…We have conducted a series of driving experiments in unstructured environments over the past several years. The first studies were a proof of concept that we could infer difficult driving situation from readily available sensors from a commercial vehicle in naturalistic on-road driving conditions [12]. The set of experiments covered by this analysis involved driving in off-road conditions, on semi-improved and unimproved paths, at the United States Marine Corps Base Camp Pendleton.…”

Section: Experimental Descriptionmentioning

confidence: 99%

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Using Context to Identify Difficult Driving Situations in Unstructured Environments

Dixon

Basilico

Forsythe

et al. 2009

Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience

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We present a context-based machine-learning approach for identifying difficult driving situations using sensor data that is readily available in commercial vehicles. The goal of this system is improve vehicle safety by alerting drivers to potentially dangerous situations. The context-based approach is a two-step learning process by first performing unsupervised learning to discover meaningful regularities, or "contexts," in the vehicle data and then performing supervised learning, mapping the current context to a measure of driving difficulty. To validate the benefit of this approach, we collected driving data from a set of experiments involving both on-road and off-road driving tasks in unstructured environments. We demonstrate that context recognition greatly improves the performance of identifying difficult driving situations and show that the driving-difficulty system achieves a human level of performance on crossvalidation data.

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Section: Related Workmentioning

confidence: 80%

Section: Experimental Descriptionmentioning

confidence: 99%

Using Context to Identify Difficult Driving Situations in Unstructured Environments

Dixon

Basilico

Forsythe

et al. 2009

Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience

View full text Add to dashboard Cite

show abstract

“…Different aspects were analyzed by [12,13] in their studies. In particular, [12] exploited machine learning to model human recognition of vehicle-driving situations.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Different aspects were analyzed by [12,13] in their studies. In particular, [12] exploited machine learning to model human recognition of vehicle-driving situations. The purpose of such research is to provide physical context to mitigate unnecessary distractions, allowing the driver to maintain focus during periods that require high concentration.…”

Section: Background and Related Workmentioning

confidence: 99%

On combining Big Data and machine learning to support eco-driving behaviours

et al. 2019

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The automotive industry is currently facing a moment of radical change. According to the 2018 edition of the future automotive industry structure (FAST) study, 1 conducted by Oliver Wyman and the German Automotive Association, there are seven main factors that will drive this sector over the next decade (until 2030), thanks to the exploitation of digitization, artificial intelligence (AI), and machine learning (ML). These identified factors are: (i) connected vehicles, (ii) autonomous vehicles, (iii) electric mobility, (iv) digital industry, (v) new distribution channel pay-per-use, (vi) changes in customer structure, and (vii) new concepts for Human-Machine Interface. In this emerging context, the Internet of Things paradigm can be exploited, taking advantage of interconnected sensors, which are more and more accurate, smaller, and powerful [1]. Accordingly, vehicles are increasingly being equipped with a variety of sensors, provided with integrated connectivity, that can monitor different components and situations, such as the engine, the driving style or the environmental conditions. Indeed, these sensors lead to the generation of large volumes of data about the vehicle,

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A scene-adaptive motion detection model based on machine learning and data clustering

Zheng

et al. 2013

Multimed Tools Appl

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Supervised machine learning for modeling human recognition of vehicle-driving situations

Cited by 6 publications

References 10 publications

Using Context to Identify Difficult Driving Situations in Unstructured Environments

Using Context to Identify Difficult Driving Situations in Unstructured Environments

On combining Big Data and machine learning to support eco-driving behaviours

A scene-adaptive motion detection model based on machine learning and data clustering

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