ROAD: THE ROAD EVENT AWARENESS DATASET FOR AUTONOMOUS DRIVING

Authors

  • Dr. M. Ramasubramanian, Anisha Kapoor, Sara Amreen, K. S. Swathi

Keywords:

Awareness Dataset (ROAD) for Autonomous Driving, YOLOv5 detectors.

Abstract

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle’s ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations.

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References

J. Winn and J. Shotton, “The layout consistent random field for recognizing and segmenting partially occluded objects,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2006, pp. 37–44.

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Published

2023-11-24

How to Cite

Dr. M. Ramasubramanian, Anisha Kapoor, Sara Amreen, K. S. Swathi. (2023). ROAD: THE ROAD EVENT AWARENESS DATASET FOR AUTONOMOUS DRIVING. Pegem Journal of Education and Instruction, 13(3), 512–523. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4047

Issue

Vol. 13 No. 3 (2023): PEGEGOG

Section

Article

License

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