Human–machine shared control for lane departure assistance based on hybrid system theory

Chen, Wuwei; Zhao, Lian; Tan, Dan; Wei, Zhenya; Xu, Kai; Jiang, Yuting

doi:10.1016/j.conengprac.2018.12.011

Cited by 25 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, haptic shared control can facilitate transitions between SAE level 3 and higher (hands-off) to manual and vice versa. Several recent studies have used elaborate assistance approaches that combine continuous and bandwidth systems ( Bian et al, 2019 ; Merah et al, 2016 ) or switch between discrete levels of automation from manual driving to autonomous driving ( Chen et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

et al. 2020

View full text Add to dashboard Cite

Objective This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. Background Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. Method Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. Results Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. Conclusion The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. Application The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

et al. 2020

View full text Add to dashboard Cite

show abstract

“…(3) For lane keeping behavior, combining previous studies [ 35 , 36 , 37 ] and the specific situation of this study, the period where

was extracted first, then the extracted segments were further verified by outside ambient videos.…”

Section: Methodology Descriptionmentioning

confidence: 99%

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

Xie

Chen

et al. 2021

IJERPH

View full text Add to dashboard Cite

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

show abstract

“…In the event that the actual weather did not match a pilot's expectations based on the weather product, the flight could be at risk, and the mismatch may have an additional effect of negative feedback to the pilot(s). Negative feedback from an automated system can erode the pilot's trust in the system [23] (pp. 399-407).…”

Section: Automationmentioning

confidence: 99%

The Effects of Display Type, Weather Type, and Pilot Experience on Pilot Interpretation of Weather Products

et al. 2021

View full text Add to dashboard Cite

The majority of general aviation (GA) accidents involving adverse weather result in fatalities. Considering the high weather-related fatality rate among GA flight operations, it is imperative to ensure that GA pilots of all experience levels can incorporate available weather information into their flight planning. In the past decade, weather product development has incorporated increasing levels of automation, which has led to the generation of high-resolution, model-based aviation displays such as graphical turbulence guidance and current icing potential, which rival the resolution of radar and satellite imagery. This is in stark contrast to the traditional polygonal-based displays of aviation weather hazards (G-AIRMETs and SIGMETs). It is important to investigate the effects of these changes on the end user. Therefore, the purpose of this study was to compare the interpretability of weather products for two areas of interest: display type (traditional polygons vs. model-based imagery) and type of weather phenomena (ceiling/visibility, turbulence, and icing), across a range of pilot experience levels. Two hundred and four participants completed a series of weather product interpretation questions. The results indicated significant effects of product display type, as well as significant effects of weather phenomena and pilot experience on product interpretation. Further investigation is needed to assess possible extraneous variables.

show abstract

Human–machine shared control for lane departure assistance based on hybrid system theory

Cited by 25 publications

References 15 publications

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

The Effects of Display Type, Weather Type, and Pilot Experience on Pilot Interpretation of Weather Products

Contact Info

Product

Resources

About