Driver's lane-change intent identification based on pupillary variation

Jang, Young-Min; Mallipeddi, Rammohan

doi:10.1109/icce.2014.6775970

Cited by 17 publications

(12 citation statements)

References 4 publications

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“…We choose 3 seconds window because it has a relatively good TPR and FPR. In addition, because we are interested in the early lane change a 3 seconds window after the onset of the C-ITS warning is a reasonable choice, especially if we consider that the 85 th percentile of the perception reaction time (PRT) of the drivers is in the range of 1.1 to 1.3 seconds [18,19].…”

Section: Classificationmentioning

confidence: 99%

“…The proposed approach achieved 96.98% classification accuracy which makes it a promising assistance system for lane-change decisions for human drivers and autonomous vehicles. Jang et al [19] [21] used data collected by a vehicle equipped with a front-facing stereo camera and several radar sensors to obtain a 360 field of view. The data was used to extract several features, which were modelled using a mixture of Gaussian distributions.…”

Section: Introductionmentioning

confidence: 99%

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A framework for testing independence between lane change and cooperative intelligent transportation system

Elhenawy¹,

Glaser²,

Bond³

et al. 2020

PLoS ONE

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Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for testing independence between lane change and cooperative intelligent transportation system

Elhenawy¹,

Glaser²,

Bond³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Drivers often concentrated on the end of the road in front their vehicles [6]. Gaze data are also used for detection of drowsiness [21,4], lane change [13,11,7], and hazard perception [1,10]. For example, results from [21] showed that gazing time is related to the drowsiness level with a statistical significance.…”

Section: Literature Reviewmentioning

confidence: 99%

Who is Better at Anticipating Traffic Crashes, Human or Artificial Intelligence? A Gaze Data-based Exploratory Study

Liu

Karim

Qin

2021

Preprint

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Enhancing roadway safety is a priority of transportation. Hence, Artificial Intelligence (AI)-powered crash anticipation is receiving growing attention, which aims to assist drivers and Automated Driving Systems (ADSs) in avoiding crashes. To gain the trust from ADS users, it is essential to benchmark the performance of AI models against humans. This paper establishes a gaze databased method with the measures and metrics for evaluating human drivers' ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers when they are watching 100 videos that include both normal and risky driving scenes. Statistical analyses (at the 0.05 significance level) of experimental data show that on average drivers can anticipate a crash up to 2.61±0.100 seconds before it occurs. The chance whereby drivers have anticipated crashes before they occur, termed humans' recall, is 0.928±0.0206. An AI model achieving the same recall value can anticipate crashes 2.22 seconds earlier than drivers on average, and the anticipation precision is 0.959. The study finds that crash involved traffic agents in driving scenes can vary drivers' instant attention level, average attention level, and spatial attention distribution. This finding supports AI models that learn a dynamic spatial-temporal attention mechanism for strengthening their ability to anticipate crashes. Results from the comparison suggests benefits of keeping human-in-the-loop, including further refining AI

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“…They concluded that a 10-second eye-gazing information window best predicts intent, and a 5-second eye-gazing information window performs best because of less noise. Young-Min Jang et al proposed a lane-change intention prediction model, based on the change in the pupil size of the human eye, suitable for an intelligent driving assistance system [14]. Intention-oriented eye tracking can be viewed as a cognitive process of information gathering, which provides an early indication of driver mental states.…”

Section: Related Workmentioning

confidence: 99%

Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario

Gao

Yan

Yang

et al. 2019

Sensors

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Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.

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Driver's lane-change intent identification based on pupillary variation

Cited by 17 publications

References 4 publications

A framework for testing independence between lane change and cooperative intelligent transportation system

A framework for testing independence between lane change and cooperative intelligent transportation system

Who is Better at Anticipating Traffic Crashes, Human or Artificial Intelligence? A Gaze Data-based Exploratory Study

Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario

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