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Schnebelen D, Reynaud E, Ouimet MC, Seguin P, Navarro J. A neuroergonomics approach to driver's cooperation with Lane Departure Warning Systems. Behav Brain Res 2024; 456:114699. [PMID: 37802390 DOI: 10.1016/j.bbr.2023.114699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/18/2023] [Accepted: 10/03/2023] [Indexed: 10/10/2023]
Abstract
Lane Departure Warning Systems (LDWS) are automation that warn drivers in case of immediate lane departure. While LDWS are associated with increased road safety, little is known about the neural aspects of the cooperation between an LDWS and the driver behind the wheel. The present study addresses this issue by combining fMRI and driving simulation for experienced and novice drivers. The results reveal brain areas activated immediately after warning: it involves areas linked to the alertness network (midbrain, thalamus, anterior cingulate cortex), to motor actions and planning (motor and premotor cortexes; BA4/6 -cerebellum) and to attentional redirection (superior frontal cortex; BA10). There were no differences between experienced and novice drivers in this network of cerebral areas. However, prior driving experience mediates the number of lane departures. The results allow for refining a model of cooperation proposed earlier in the literature, by adding a cerebral dimension.
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Affiliation(s)
- Damien Schnebelen
- Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69676 Bron, France
| | - Emanuelle Reynaud
- Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69676 Bron, France
| | - Marie Claude Ouimet
- Faculté de médecine et des sciences de la santé, Université de Sherbrooke, Canada
| | - Perrine Seguin
- Inserm U1028, CNRS UMR5292, Lyon Neuroscience Research Center, CRNL, Lyon, France
| | - Jordan Navarro
- Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69676 Bron, France; Institut Universitaire de France, France.
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Navarro J, Reynaud E, Pelerin M, Ouimet MC, Gabaude C, Schnebelen D. Visual and steering behaviours during lane departures: a longitudinal study of interactions between lane departure warning system, driving task and driving experience. ERGONOMICS 2024; 67:81-94. [PMID: 37074777 DOI: 10.1080/00140139.2023.2205620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/18/2023] [Indexed: 05/03/2023]
Abstract
Lane Departure Warning Systems (LDWS) generate a warning in case of imminent lane departure. LDWS have proven to be effective and associated human-machine cooperation modelled. In this study, LDWS acceptance and its impact on visual and steering behaviour have been investigated over 6 weeks for novice and experienced drivers. Unprovoked lane departures were analysed along three driving tasks gradually more demanding. These observations were compared to a baseline condition without automation. The number of lane departures and their duration were dramatically reduced by LDWS, and a narrower visual spread of search during lane departure events was recorded. The findings confirmed LDWS effectiveness and suggested that these benefits are supported by visuo-attentional guidance. No specific influence of driving experience on LDWS was found, suggesting that similar cognitive processes are engaged with or without driving experience. Drivers' acceptance of LDWS lowered after automation use, but LDWS effectiveness remained stable during prolonged use.Practitioner summary: Lane Departure Warning Systems (LDWS) have been designed to prevent lane departure crashes. Here, LDWS assessment over a 6-week period showed a major drop in the number of lane departure events increasing over time. LDWS effectiveness is supported by the guidance of drivers' visual attention during lane departure events.
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Affiliation(s)
- Jordan Navarro
- Laboratoire d'Etude des Mécanismes Cognitifs, Université Lumière Lyon 2, Lyon, France
- Institut Universitaire de France, Paris, France
| | - Emanuelle Reynaud
- Laboratoire d'Etude des Mécanismes Cognitifs, Université Lumière Lyon 2, Lyon, France
| | - Maëlle Pelerin
- Laboratoire d'Etude des Mécanismes Cognitifs, Université Lumière Lyon 2, Lyon, France
| | - Marie Claude Ouimet
- Faculté de Médecine et des Sciences de la Santé, University of Sherbrooke, Sherbrooke, Canada
| | - Catherine Gabaude
- Univ Gustave Eiffel, Université Paris Cité, LaPEA, Versailles, France
| | - Damien Schnebelen
- Laboratoire d'Etude des Mécanismes Cognitifs, Université Lumière Lyon 2, Lyon, France
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Navarro J, Reynaud E, Ouimet MC, Schnebelen D. Comparison of Experienced and Novice Drivers' Visual and Driving Behaviors during Warned or Unwarned Near-Forward Collisions. SENSORS (BASEL, SWITZERLAND) 2023; 23:8150. [PMID: 37836979 PMCID: PMC10575380 DOI: 10.3390/s23198150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/12/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
Abstract
Forward collision warning systems (FCWSs) monitor the road ahead and warn drivers when the time to collision reaches a certain threshold. Using a driving simulator, this study compared the effects of FCWSs between novice drivers (unlicensed drivers) and experienced drivers (holding a driving license for at least four years) on near-collision events, as well as visual and driving behaviors. The experimental drives lasted about six hours spread over six consecutive weeks. Visual behaviors (e.g., mean number of fixations) and driving behaviors (e.g., braking reaction times) were collected during unprovoked near-collision events occurring during a car-following task, with (FCWS group) or without FCWS (No Automation group). FCWS presence reduced the number of near-collision events drastically and enhanced visual behaviors during those events. Unexpectedly, brake reaction times were observed to be significantly longer with FCWS, suggesting a cognitive cost associated with the warning process. Still, the FCWS showed a slight safety benefit for novice drivers attributed to the assistance provided for the situation analysis. Outside the warning events, FCWS presence also impacted car-following behaviors. Drivers took an extra safety margin, possibly to prevent incidental triggering of warnings. The data enlighten the nature of the cognitive processes associated with FCWSs. Altogether, the findings support the general efficiency of FCWSs observed through a massive reduction in the number of near-collision events and point toward the need for further investigations.
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Affiliation(s)
- Jordan Navarro
- Laboratoire d’Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69007 Lyon, France; (E.R.); (D.S.)
- Institut Universitaire de France, 75005 Paris, France
| | - Emanuelle Reynaud
- Laboratoire d’Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69007 Lyon, France; (E.R.); (D.S.)
| | - Marie Claude Ouimet
- Faculté de Médecine Et des Sciences de La Santé, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada;
| | - Damien Schnebelen
- Laboratoire d’Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, 69007 Lyon, France; (E.R.); (D.S.)
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Why Does the Automation Say One Thing but Does Something Else? Effect of the Feedback Consistency and the Timing of Error on Trust in Automated Driving. INFORMATION 2022. [DOI: 10.3390/info13100480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, first experiences with automation, either positive or not, are essential for drivers to calibrate their level of trust. On the other hand, an automation that provides feedback about its own level of capability to handle a specific driving situation may also help drivers to calibrate their level of trust. The reported experiment was undertaken to examine how the combination of these two effects will impact the driver trust calibration process. Four groups of drivers were randomly created. Each experienced either an early (i.e., directly after the beginning of the drive) or a late (i.e., directly before the end of it) critical situation that was poorly handled by the automation. In addition, they experienced either a consistent continuous feedback (i.e., that always correctly informed them about the situation), or an inconsistent one (i.e., that sometimes indicated dangers when there were none) during an automated drive in a driving simulator. Results showed the early- and poorly-handled critical situation had an enduring negative effect on drivers’ trust development compared to drivers who did not experience it. While being correctly understood, inconsistent feedback did not have an effect on trust during properly managed situations. These results suggest that the performance of the automation has the most severe influence on trust, and the automation’s feedback does not necessarily have the ability to influence drivers’ trust calibration during automated driving.
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Hang J, Yan X, Li X, Duan K, Yang J, Xue Q. An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment. JOURNAL OF SAFETY RESEARCH 2022; 80:416-427. [PMID: 35249623 DOI: 10.1016/j.jsr.2021.12.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 12/19/2021] [Indexed: 06/14/2023]
Abstract
INTRODUCTION To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety. METHOD By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. RESULTS The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of -7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation.
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Affiliation(s)
- Junyu Hang
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Xuedong Yan
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Xiaomeng Li
- Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Institute of Health and Biomedical Innovation (IHBI), Kelvin Grove, Queensland 4059, Australia.
| | - Ke Duan
- MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, PR China.
| | - Jingsi Yang
- CRSC Communication & Information Group Company Ltd., Beijing 100070, PR China.
| | - Qingwan Xue
- Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China.
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Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review. JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING (ENGLISH EDITION) 2020. [DOI: 10.1016/j.jtte.2020.10.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Li Q, Zhou J, Li B, Guo Y, Xiao J. Robust Lane-Detection Method for Low-Speed Environments. SENSORS 2018; 18:s18124274. [PMID: 30518167 PMCID: PMC6308961 DOI: 10.3390/s18124274] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 11/28/2018] [Accepted: 12/02/2018] [Indexed: 11/16/2022]
Abstract
Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.
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Affiliation(s)
- Qingquan Li
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.
- College of Civil Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Jian Zhou
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Bijun Li
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Yuan Guo
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Jinsheng Xiao
- School of Electronic Information, Wuhan University, Wuhan 430072, China.
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Navarro J, Heuveline L, Avril E, Cegarra J. Influence of human-machine interactions and task demand on automation selection and use. ERGONOMICS 2018; 61:1601-1612. [PMID: 30010501 DOI: 10.1080/00140139.2018.1501517] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 07/04/2018] [Indexed: 06/08/2023]
Abstract
A seminal work by Sheridan and Verplank depicted 10 levels of automation, ranging from no automation to an automation that acts completely autonomously without human support. These levels of automation were later complemented with a four-stage model of human information processing. Next, human-machine cooperation centred models and associated cooperation modes were introduced. The objective of the experiment was to test which human-machine theorie describe automation use better. The participants were asked to choose repeatedly between four automation types (i.e. no automation, warning, co-action, function delegation) to complete three multi-attribute task battery tasks. The results showed that the participants favour the selection of automation types offering the best human-machine interactions quality rather that the most effective automation type. Contrary to human-machine cooperation models, technology centred models could not predict accurately automation selection. The most advanced automation was not the most selected. Practitioner Summary: The experiment dealt with how people select different automation types to complete the multi-attribute task battery that emulates recreational aircraft pilot tasks. Automation performance was not the main criteria that explain automation use, as people tend to select an automation type based on the quality of the human-machine cooperation.
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Affiliation(s)
- Jordan Navarro
- a Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082) , University Lyon 2 , Bron, France
- b Institut Universitaire de France , Paris , France
| | - Louis Heuveline
- a Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082) , University Lyon 2 , Bron, France
| | - Eugénie Avril
- c Laboratoire Sciences de la Cognition, Technologie, Ergonomie (SCoTE EA 7420) , Université de Toulouse, INU Champollion , Albi , France
| | - Julien Cegarra
- c Laboratoire Sciences de la Cognition, Technologie, Ergonomie (SCoTE EA 7420) , Université de Toulouse, INU Champollion , Albi , France
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Affiliation(s)
- Jordan Navarro
- Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), University Lyon 2, Bron, France
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