Detecting lane departures from steering wheel signal

Sandström, Max; Lampsijärvi, Eetu; Holmstrom, Axi; Maconi, Göran; Ahmadzai, Shabana; Meriläinen, Antti; Hæggström, Edward; Forsman, Pia

doi:10.1016/j.aap.2016.12.003

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Examples of the studies are the reported crash of LDW due to drivers' age (Cicchino, 2018) and the effectiveness of heads-up LDW for younger and older drivers (Aksan et al, 2017). The concentration of research regarding the LDW system itself has become less attractive due to the passivity of the system's feedback (Sandstrom et al, 2017). In addition, there is also limited publication regarding the influence of the environment on the LDW system and it is generally focusing on specific geographical locations.…”

Section: Literature Reviewmentioning

confidence: 99%

Lane Departure Warning and Lane Keep Assist Assessment based on Southeast Asian Environmental Conditions: Preliminary Investigation

Mansor

Nurfaizey

Masripan

et al. 2020

JSAEM

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Lane Departure Warning (LDW) and Lane Keep Assist (LKA) systems are part of the Advanced Driver-Assistance Systems (ADAS), which are equipped in the latest passenger vehicles sold in the Southeast Asia (SEA) countries. Both technologies are very beneficial to gain improved safety performance for vehicle occupants and benefiting other road users such as pedestrians and cyclists by alerting the driver and making automatic trajectory correction when the vehicle is deviating away from the correct path while in motion. Nevertheless, there is yet any test protocol established by the New Car Assessment Program for Southeast Asian Countries (ASEAN NCAP) to evaluate both technologies tailored to the SEA’s environmental conditions. Hence, this is a preliminary investigation of the new test protocol developed for LDW and LKA based on SEA’s environmental conditions. The proposed protocol incorporated the effect of both dry and wet conditions, which is unique to simulate the main weather elements in the region. On-road tests using actual passenger vehicles were conducted using a rain simulator to simulate the rainy weather. The preliminary tests were performed on straight road conditions. Results show that the proposed protocol was able to assess the effectiveness of LDW and LKA in both dry and wet conditions. Further actions shall be carried out in the later stage of this project to validate the proposed protocol using other vehicle models sold in SEA that equipped with similar technologies.

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Section: Literature Reviewmentioning

confidence: 99%

Lane Departure Warning and Lane Keep Assist Assessment based on Southeast Asian Environmental Conditions: Preliminary Investigation

Mansor

Nurfaizey

Masripan

et al. 2020

JSAEM

View full text Add to dashboard Cite

show abstract

“…For the lane departure problem, Sandström et al [12] created a method with signals from the steering wheel, avoiding loss in weather conditions and bad roads. Satzoda et al [13] provided a low-cost video-based system designed to assist the driver by issuing warnings during lane drifting and lane changes implemented on the Snapdragon TM embedded computing processor setup with the camera on top of the windshield.…”

Section: Related Workmentioning

confidence: 99%

“…Sensitivity to light is a relevant factor in the lane departure detection and methods based on computer vision usually suffer from light interference. At this point, different works [12,16,17] do not suffer interference. Our proposal does not suffer interference, but it presents some disadvantages, like the dependence on the sensitivity of the steering wheel of the vehicle for the calibration, needing to calibrate the steering wheel a first time.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Driver Monitoring through the Statistical Process Control

Assuncao

Aquino

Santos

et al. 2019

Sensors

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This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver’s behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ≈60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations.

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“…By noting the tendency of people to yawn frequently when they felt sleepy, a study proposed a yawn detection-based estimation technique, in which a camera, for identifying when a driver yawns, is placed under a car's interior mirror [1]. Drowsiness while driving can also be detected using a trajectory sensor [2,3] that monitors a vehicle's movement, as a driver's control becomes erratic if they are drowsy. Other researchers have proposed the use of biological data, such as electrocardiogram (ECG) [4][5][6], electrooculogram (EOG) [7][8][9], respiratory [10,11], electromyogram (EMG) [12] and electroencephalogram (EEG) [13][14][15][16] signals to assess drowsiness.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

Akbar

Igasaki

2019

Information

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As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

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Detecting lane departures from steering wheel signal

Cited by 12 publications

References 16 publications

Lane Departure Warning and Lane Keep Assist Assessment based on Southeast Asian Environmental Conditions: Preliminary Investigation

Lane Departure Warning and Lane Keep Assist Assessment based on Southeast Asian Environmental Conditions: Preliminary Investigation

Vehicle Driver Monitoring through the Statistical Process Control

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

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