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Zgonnikov A, Abbink D. Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers. HUMAN FACTORS 2024; 66:1399-1413. [PMID: 36534014 PMCID: PMC10958748 DOI: 10.1177/00187208221144561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. BACKGROUND Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. RESULTS The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. CONCLUSION Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. APPLICATION Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.
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Park Y, Ji J, Kang H. Effect of a looming visual cue on situation awareness and perceived urgency in response to a takeover request. Heliyon 2024; 10:e23053. [PMID: 38173484 PMCID: PMC10761363 DOI: 10.1016/j.heliyon.2023.e23053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 01/05/2024] Open
Abstract
This study aimed to investigate the effect of a looming visual cue on situation awareness and perceived urgency in response to a takeover request (TOR), and to explore the underlying mechanisms of this effect through three experiments. In Experiment 1, the optimal size and speed of a red disk were determined, which were effective in capturing looming motion and conveying the urgency of the situation. The results indicated that both looming speed and size ratio had significant effects on situation awareness and perceived urgency. In Experiment 2, the effects of looming stimuli were compared with dimming stimuli, and the results showed that the looming visual cue was more effective in promoting perceived urgency and situation awareness. The results also indicated that the looming visual cue attracted more visual attention than the dimming visual cue, in line with previous studies. Experiment 3 utilized a driving simulator to test the effectiveness of the looming visual cue in promoting fast and appropriate responses to TORs in complex driving scenarios. The results showed that the looming visual cue was more effective in promoting perceived urgency and enhancing situation awareness, especially in highly complex driving situations. Overall, the findings suggest that the looming visual cue is a powerful tool for promoting fast and appropriate responses to TORs and enhancing situation awareness, particularly in complex driving scenarios. These results have important implications for designing effective TOR systems and improving driver safety on the road.
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Affiliation(s)
- YounJung Park
- Global Convergence Content Research Center, Sungkyunkwan University, South Korea
| | - Jeayeong Ji
- Samsung Electronics, South Korea
- Stanford Center at the Incheon Global Campus, Stanford University, South Korea
| | - Hyunmin Kang
- Stanford Center at the Incheon Global Campus, Stanford University, South Korea
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Kar P, Kumar S, Samalla S, Chunchu M, Ravi Shankar KVR. Exploratory analysis of evasion actions of powered two-wheeler conflicts at unsignalized intersection. ACCIDENT; ANALYSIS AND PREVENTION 2024; 194:107363. [PMID: 37918091 DOI: 10.1016/j.aap.2023.107363] [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: 06/19/2023] [Revised: 10/15/2023] [Accepted: 10/22/2023] [Indexed: 11/04/2023]
Abstract
The study investigates the braking and steering evasions of powered two-wheelers (PTWs) during severe conflicts observed at an unsignalized intersection. Traffic conflicts were detected using a surrogate safety indicator called anticipated collision time (ACT). Then the peak-over-threshold approach was used to identify the severe conflicts and the evasive actions. Conflicts between right-turning PTWs and through-moving vehicles, through-moving PTWs crossing through-moving vehicles, and merging/diverging PTWs were analyzed using the minimum ACT (ACTmin), maximum deceleration rate (DRmax), maximum yaw rate (YRmax), and time of evasive action (TEA). The evasive actions were classified into five categories: driver/rider error, no-evasion, braking-only, steering-only, and both braking and steering. Analysis reveals that right-turning PTWs experience higher crash risk (0.7 %) than the other movements. PTW riders primarily employ extreme steering maneuvers (greater than 13 degrees/s) to evade conflicts, whereas braking rates lie in the normal ranges (less than 1.5 m/s2). The time of evasive action varies between 2.04 and 2.44 s, with the right-turning PTW riders responding early. Through-moving riders commit errors while evading severe conflicts and perform fewer evasive actions than right-turning and merging/diverging riders. Right-turning riders perform more steering-only evasions than braking-only, whereas the riders involved in the other two conflicts execute more braking-only evasions. These findings suggest that conflict type influences riders' braking and steering responses. Hence, future applications in advanced driver/rider assistance systems and training programs should consider appropriate evasive action strategies for different conflict types.
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Affiliation(s)
- Pranab Kar
- Indian Institute of Technology Guwahati, India.
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Durrani U, Lee C. Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions. JOURNAL OF SAFETY RESEARCH 2023; 86:298-310. [PMID: 37718057 DOI: 10.1016/j.jsr.2023.07.008] [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: 08/03/2022] [Revised: 04/05/2023] [Accepted: 07/14/2023] [Indexed: 09/19/2023]
Abstract
INTRODUCTION The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction. METHOD Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off). RESULTS The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on. CONCLUSIONS This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk. PRACTICAL APPLICATIONS The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.
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Affiliation(s)
- Umair Durrani
- Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Windsor, Canada.
| | - Chris Lee
- Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Windsor, Canada.
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Guo H, Xie K, Keyvan-Ekbatani M. Modeling driver's evasive behavior during safety-critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning. ACCIDENT; ANALYSIS AND PREVENTION 2023; 186:107063. [PMID: 37023652 DOI: 10.1016/j.aap.2023.107063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Lane changes are complex driving behaviors and frequently involve safety-critical situations. This study aims to develop a lane-change-related evasive behavior model, which can facilitate the development of safety-aware traffic simulations and predictive collision avoidance systems. Large-scale connected vehicle data from the Safety Pilot Model Deployment (SPMD) program were used for this study. A new surrogate safety measure, two-dimensional time-to-collision (2D-TTC), was proposed to identify the safety-critical situations during lane changes. The validity of 2D-TTC was confirmed by showing a high correlation between the detected conflict risks and the archived crashes. A deep deterministic policy gradient (DDPG) algorithm, which could learn the sequential decision-making process over continuous action spaces, was used to model the evasive behaviors in the identified safety-critical situations. The results showed the superiority of the proposed model in replicating both the longitudinal and lateral evasive behaviors.
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Affiliation(s)
- Hongyu Guo
- Complex Transport Systems Laboratory (CTSLAB), Department of Civil and Natural Resources Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
| | - Kun Xie
- Transportation Informatics Lab, Department of Civil and Environmental Engineering, Old Dominion University, 4635 Hampton Boulevard, Norfolk, VA 23529, United States.
| | - Mehdi Keyvan-Ekbatani
- Complex Transport Systems Laboratory (CTSLAB), Department of Civil and Natural Resources Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
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A Simulation-based Study of the Effect of Brake Light Flashing Frequency on Driver Brake Behavior from the Perspective of Response Time. Behav Sci (Basel) 2022; 12:bs12090332. [PMID: 36135136 PMCID: PMC9495388 DOI: 10.3390/bs12090332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 11/16/2022] Open
Abstract
To prevent vehicle crashes, studies have proposed the use of flashing signals (brake lights or other light indicators) to improve the driver’s response time when the leading vehicle is braking. However, there are no consistent results on the ideal flashing frequency of the brake lights. This study aimed to investigate different brake light flashing frequencies to assess their impact on braking response time. Twenty-four participants aged 25 to 30 were recruited. Two driving speed environments (50 and 80 km/h), three deceleration rates (0.25, 0.6, and 1 g), and four brake light flashing frequencies (0, 2, 4, and 7 Hz) were examined. Braking response time, average braking force, and braking response time ratio were used to evaluate the driving behavior. The results showed that the braking response time and average braking force were affected by the deceleration rate in the 50 km/h driving environment. In the 50 and 80 km/h driving environments, although there were no significant differences among the three deceleration rates, the braking response time decreased by 3–7% under the flashing brake light condition. These findings can be used as a reference for safety designs as well as future studies on driving behavior.
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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: 2.0] [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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Markkula G, Uludağ Z, Wilkie RM, Billington J. Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection. PLoS Comput Biol 2021; 17:e1009096. [PMID: 34264935 PMCID: PMC8282001 DOI: 10.1371/journal.pcbi.1009096] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/19/2021] [Indexed: 11/24/2022] Open
Abstract
Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate-the visual looming-of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.
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Affiliation(s)
- Gustav Markkula
- Institute for Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Zeynep Uludağ
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | | | - Jac Billington
- School of Psychology, University of Leeds, Leeds, United Kingdom
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Sarkar A, Hickman JS, McDonald AD, Huang W, Vogelpohl T, Markkula G. Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach. ACCIDENT; ANALYSIS AND PREVENTION 2021; 154:106055. [PMID: 33691227 DOI: 10.1016/j.aap.2021.106055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 12/28/2020] [Accepted: 02/19/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE The paper presents a systematic analysis of drivers' crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context. METHODS We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers' gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers' pre-incident behavior (secondary behavior, gaze behavior), and drivers' perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver. RESULTS The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers' last glance duration before the event, last glance eccentricity, duration of 'eyes on road' immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers' maneuver intention. CONCLUSIONS In this paper we analyzed driving context, drivers' behavior, event criticality, and drivers' response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers' maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.
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Affiliation(s)
| | | | - Anthony D McDonald
- Texas A&M University, United States; Texas A&M Transportation Institute, United States
| | - Wenyan Huang
- Virginia Polytechnic Institute and State University, United States
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Durrani U, Lee C, Shah D. Predicting driver reaction time and deceleration: Comparison of perception-reaction thresholds and evidence accumulation framework. ACCIDENT; ANALYSIS AND PREVENTION 2021; 149:105889. [PMID: 33248429 DOI: 10.1016/j.aap.2020.105889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 06/12/2023]
Abstract
Prediction of driver reaction to the lead vehicle motion based on the perception-reaction time (PRT) is critical for prediction of rear-end crash risk. This study determines PRT at various spacings in approaching and braking conditions, and examines the association of PRT and deceleration rate with crash risk. For these tasks, a total of 50 drivers' behavior was observed in a driving simulator experiment with 4 different scenarios - reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle's speed change, and perception of a slow-moving lead vehicle. The study tested three hypotheses of PRT including perception and reaction thresholds and the evidence accumulation framework using a visual variable (tau-inverse). It was found that the drivers neither reacted after a specific PRT from the start of perception nor reacted at a specific value of tau-inverse. Rather, the drivers generally reacted when the accumulation of evidence (tau-inverse) over time reached a threshold. It was also found that the magnitude of deceleration rate depends on the tau-inverse at the start of braking and hence, higher crash risk was associated with higher level of urgency and insufficient brake force rather than longer PRT. This study demonstrates that the evidence accumulation framework is a promising method of predicting driver reaction in approaching and braking conditions for different types of lead vehicle, and the level of urgency is important for predicting the probability of crash.
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Affiliation(s)
- Umair Durrani
- Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Canada.
| | - Chris Lee
- Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Canada.
| | - Dhwani Shah
- Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Canada.
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Mole C, Pekkanen J, Sheppard W, Louw T, Romano R, Merat N, Markkula G, Wilkie R. Predicting takeover response to silent automated vehicle failures. PLoS One 2020; 15:e0242825. [PMID: 33253219 PMCID: PMC7703974 DOI: 10.1371/journal.pone.0242825] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 11/10/2020] [Indexed: 11/18/2022] Open
Abstract
Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.
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Affiliation(s)
- Callum Mole
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Jami Pekkanen
- School of Psychology, University of Leeds, Leeds, United Kingdom
- Cognitive Science, University of Helsinki, Helsinki, Finland
| | - William Sheppard
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Tyron Louw
- Institute of Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Richard Romano
- Institute of Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Natasha Merat
- Institute of Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Gustav Markkula
- Institute of Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Richard Wilkie
- School of Psychology, University of Leeds, Leeds, United Kingdom
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Bianchi Piccinini G, Lehtonen E, Forcolin F, Engström J, Albers D, Markkula G, Lodin J, Sandin J. How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models. HUMAN FACTORS 2020; 62:1212-1229. [PMID: 31590570 DOI: 10.1177/0018720819875347] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). BACKGROUND Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. METHOD Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. RESULTS The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. CONCLUSION Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. APPLICATION Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.
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Affiliation(s)
| | - Esko Lehtonen
- Chalmers University of Technology, Gothenburg, Sweden
| | | | | | - Deike Albers
- Chalmers University of Technology, Gothenburg, Sweden
| | | | - Johan Lodin
- Volvo Group Trucks Technology, Gothenburg, Sweden
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McDonald AD, Alambeigi H, Engström J, Markkula G, Vogelpohl T, Dunne J, Yuma N. Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures. HUMAN FACTORS 2019; 61:642-688. [PMID: 30830804 DOI: 10.1177/0018720819829572] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
OBJECTIVE This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. BACKGROUND Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. METHOD Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. RESULTS The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. CONCLUSION Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. APPLICATION Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.
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Miletić S, van Maanen L. Caution in decision-making under time pressure is mediated by timing ability. Cogn Psychol 2019; 110:16-29. [DOI: 10.1016/j.cogpsych.2019.01.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 11/21/2018] [Accepted: 01/23/2019] [Indexed: 12/22/2022]
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