Drivers use active gaze to monitor waypoints during automated driving

Mole, Callum; Pekkanen, Jami; Sheppard, William E. A.; Markkula, Gustav; Wilkie, Richard M.

doi:10.1038/s41598-020-80126-2

Cited by 11 publications

(12 citation statements)

References 79 publications

(234 reference statements)

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“…Two datasets were taken from two separate driving simulator experiments with human participants, where steering and eye movements were recorded (See Figure 2 for sample screenshots). These data were used to simulate steering-by-gaze behaviors (components of these datasets were first presented in Mole et al. [2021] and Tuhkanen et al.…”

Section: Methodsmentioning

confidence: 99%

“…More detailed descriptions of the equipment and procedures can be found in two previously published studies. Experiment 1 data were collected at the University of Leeds with the same experimental procedure as described in Mole et al. (2021) , but in the present article, only the “slalom” sections of each trial were used (the empty straight and bending portions of each trial were not included).…”

Section: Methodsmentioning

confidence: 99%

“…Typical gaze behavior during steering is a repeating pattern of eye movements, comprising smooth pursuit tracking of a point on the ground (for approximately 0.5 s) followed by a saccade to a new point at a time headway 1–3 s ahead ( Lappi & Lehtonen, 2013 ; Lappi, Pekkanen, & Itkonen, 2013 ; Lehtonen, Lappi, Kotkanen, & Summala, 2013 ; Tuhkanen et al., 2019 ). This pattern is intermittently broken by forward-polling “look-ahead” fixations further ahead ( Lehtonen et al., 2013 ; Mole, Pekkanen, Sheppard, Markkula, & Wilkie, 2021 ), return fixations closer to the observer ( Navarro et al., 2021 ), and scanning of the scenery and vehicle instruments (for review, see Lappi, 2022 ). Such gaze patterns have been observed in other contexts such as walking ( Imai, Moore, Raphan, & Cohen, 2001 ; Matthis, Yates, & Hayhoe, 2018 ) and cycling ( Vansteenkiste, Cardon, D'Hondt, Philippaerts, & Lenoir, 2013 ) and therefore seem to represent a general, robust visual strategy during locomotion.…”

Section: Introductionmentioning

confidence: 99%

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Can gaze control steering?

et al. 2023

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When steering a trajectory, we direct our gaze to locations (1–3 s ahead) that we want to steer through. How and why are these active gaze patterns conducive to successful steering? While various sources of visual information have been identified that could support steering control, the role of stereotypical gaze patterns during steering remains unclear. Here, experimental and computational approaches are combined to investigate a possible direct connection between gaze and steering: Is there enough information in gaze direction that it could be used in isolation to steer through a series of waypoints? For this, we test steering models using waypoints supplied from human gaze data, as well as waypoints specified by optical features of the environment. Steering-by-gaze was modeled using a “pure-pursuit” controller (computing a circular trajectory toward a steering point), or a simple “proportional” controller (yaw-rate set proportional to the visual angle of the steering point). Both controllers produced successful steering when using human gaze data as the input. The models generalized using the same parameters across two scenarios: (a) steering through a slalom of three visible waypoints located within lane boundaries and (b) steering a series of connected S bends comprising visible waypoints without a visible road. While the trajectories on average broadly matched those generated by humans, the differences in individual trajectories were not captured by the models. We suggest that “looking where we are going” provides useful information and that this can often be adequate to guide steering. Capturing variation in human steering responses, however, likely requires more sophisticated models or additional sensory information.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can gaze control steering?

et al. 2023

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“…Nevertheless, using just vehicle dynamics to measure driving styles seems not very comprehensive. Thus, many studies shift to the driver-behavior oriented and scene-specific methods for the aggressiveness estimation (Solovey et al, 2014;Munir et al, 2020;Mole et al, 2021) . In recent years, some new elements are introduced to the discussions of the evaluation of aggressiveness.…”

Section: The Estimation Of Driving Aggressivenessmentioning

confidence: 99%

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Zhang¹,

Hang²,

Huang³

et al. 2021

Preprint

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Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: lyuchen@ntu.edu.sg

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“…Thus, many studies shifted to the driver behavior‐oriented and scene‐specific methods. [ 35–37 ] In recent years, some new elements were introduced in evaluating aggressiveness. Many new explorations were conducted from aspect of scenes (straight road, [ 38 ] curves, [ 28 ] and roundabout [ 39 ] ) and human factors (hand, [ 40 ] eye, [ 41 ] EEG [ 42 ] ).…”

Section: Introductionmentioning

confidence: 99%

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

Zhang

Hang

Huang

et al. 2022

Advanced Intelligent Systems

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Interacting with surrounding road users is a key feature of autonomous vehicles and is critical for their intelligence testing. Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart, and they barely reflect human‐like behaviors in real‐world driving scenarios. Therefore, a novel hierarchical game‐theoretical framework is presented to represent naturalistic multimodal interactions among road users during simulation and testing, which is subsequently validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is used to model the decision‐making process. Next, a probing behavior is generated by the proposed game‐theoretic model, and is further applied to control the vehicle via the Markov chain. The proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human‐likeness of the proposed algorithm. The experimental results show that the proposed Bayesian game‐theoretic framework can effectively generate representative scenes of human‐like decision‐making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.163482630.00283278.

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Drivers use active gaze to monitor waypoints during automated driving

Cited by 11 publications

References 79 publications

Can gaze control steering?

Can gaze control steering?

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Human‐Like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game‐Theoretic Approach with Turing Test

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