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Drivers’ Manoeuvre Prediction for Safe HRI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Drivers’ Manoeuvre Prediction for Safe HRI. / López Pulgarín, Erwin José; Herrmann, Guido; Leonards, Ute.

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018): Proceedings of a meeting held 1-5 October 2018, Madrid, Spain. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 8609-8614.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

López Pulgarín, EJ, Herrmann, G & Leonards, U 2019, Drivers’ Manoeuvre Prediction for Safe HRI. in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018): Proceedings of a meeting held 1-5 October 2018, Madrid, Spain. Institute of Electrical and Electronics Engineers (IEEE), pp. 8609-8614. https://doi.org/10.1109/IROS.2018.8593957

APA

López Pulgarín, E. J., Herrmann, G., & Leonards, U. (2019). Drivers’ Manoeuvre Prediction for Safe HRI. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018): Proceedings of a meeting held 1-5 October 2018, Madrid, Spain (pp. 8609-8614). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IROS.2018.8593957

Vancouver

López Pulgarín EJ, Herrmann G, Leonards U. Drivers’ Manoeuvre Prediction for Safe HRI. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018): Proceedings of a meeting held 1-5 October 2018, Madrid, Spain. Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 8609-8614 https://doi.org/10.1109/IROS.2018.8593957

Author

López Pulgarín, Erwin José ; Herrmann, Guido ; Leonards, Ute. / Drivers’ Manoeuvre Prediction for Safe HRI. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018): Proceedings of a meeting held 1-5 October 2018, Madrid, Spain. Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 8609-8614

Bibtex

@inproceedings{cbca72f1f88b4eac900112b046454528,
title = "Drivers’ Manoeuvre Prediction for Safe HRI",
abstract = "Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to humanvehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decisionmaking processes. These are then integrated into the robot’s/vehicle’s own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human’s intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture) gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95{\%} for manoeuvre classification and 90{\%} for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.",
author = "{L{\'o}pez Pulgar{\'i}n}, {Erwin Jos{\'e}} and Guido Herrmann and Ute Leonards",
year = "2019",
month = "3",
doi = "10.1109/IROS.2018.8593957",
language = "English",
isbn = "9781538680933",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "8609--8614",
booktitle = "2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)",
address = "United States",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Drivers’ Manoeuvre Prediction for Safe HRI

AU - López Pulgarín, Erwin José

AU - Herrmann, Guido

AU - Leonards, Ute

PY - 2019/3

Y1 - 2019/3

N2 - Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to humanvehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decisionmaking processes. These are then integrated into the robot’s/vehicle’s own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human’s intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture) gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95% for manoeuvre classification and 90% for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.

AB - Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to humanvehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decisionmaking processes. These are then integrated into the robot’s/vehicle’s own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human’s intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture) gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95% for manoeuvre classification and 90% for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.

U2 - 10.1109/IROS.2018.8593957

DO - 10.1109/IROS.2018.8593957

M3 - Conference contribution

SN - 9781538680933

SP - 8609

EP - 8614

BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -