Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles
Anticipating a human collaborator’s intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver’s intention needs to be an essential part of A...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2020
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| _version_ | 1797086707541082112 |
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| author | Wu, M Louw, T Lahijanian, M Ruan, W Huang, X Merat, N Kwiatkowska, M |
| author_facet | Wu, M Louw, T Lahijanian, M Ruan, W Huang, X Merat, N Kwiatkowska, M |
| author_sort | Wu, M |
| collection | OXFORD |
| description | Anticipating a human collaborator’s intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver’s intention needs to be an essential part of Advanced Driver Assistance System (ADAS) design. To this end, we propose a framework that exploits drivers’ time-series eye gaze and fixation patterns to anticipate their real-time intention over possible future manoeuvres, enabling a smart and collaborative ADAS that can aid drivers to overcome safety-critical situations. The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers’ intentions about 3 seconds beforehand with over 90% accuracy. |
| first_indexed | 2024-03-07T02:25:47Z |
| format | Conference item |
| id | oxford-uuid:a58d4e07-1556-46ad-82ba-653325d7439f |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:25:47Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:a58d4e07-1556-46ad-82ba-653325d7439f2022-03-27T02:41:16ZGaze-based intention anticipation over driving manoeuvres in semi-autonomous vehiclesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:a58d4e07-1556-46ad-82ba-653325d7439fSymplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2020Wu, MLouw, TLahijanian, MRuan, WHuang, XMerat, NKwiatkowska, MAnticipating a human collaborator’s intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver’s intention needs to be an essential part of Advanced Driver Assistance System (ADAS) design. To this end, we propose a framework that exploits drivers’ time-series eye gaze and fixation patterns to anticipate their real-time intention over possible future manoeuvres, enabling a smart and collaborative ADAS that can aid drivers to overcome safety-critical situations. The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers’ intentions about 3 seconds beforehand with over 90% accuracy. |
| spellingShingle | Wu, M Louw, T Lahijanian, M Ruan, W Huang, X Merat, N Kwiatkowska, M Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title | Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title_full | Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title_fullStr | Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title_full_unstemmed | Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title_short | Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles |
| title_sort | gaze based intention anticipation over driving manoeuvres in semi autonomous vehicles |
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