Scipediacontent: Scipediacontent moved page Draft Content 221951367 to Satoh et al 2018a

2021-02-02T01:19:15Z

Scipediacontent moved page Draft Content 221951367 to Satoh et al 2018a

Scipediacontent: Created page with " == Abstract == Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, t..."

2021-02-02T01:19:10Z

Created page with " == Abstract == Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, t..."

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== Abstract ==

Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).

Comment: Accepted to ICRA 2018

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1804.02555 http://arxiv.org/abs/1804.02555]

* [http://arxiv.org/pdf/1804.02555 http://arxiv.org/pdf/1804.02555]

* [http://dx.doi.org/10.1109/icra.2018.8460812 http://dx.doi.org/10.1109/icra.2018.8460812]

* [http://xplorestaging.ieee.org/ielx7/8449910/8460178/08460812.pdf?arnumber=8460812 http://xplorestaging.ieee.org/ielx7/8449910/8460178/08460812.pdf?arnumber=8460812],
: [http://dx.doi.org/10.1109/icra.2018.8460812 http://dx.doi.org/10.1109/icra.2018.8460812]

* [https://dblp.uni-trier.de/db/journals/corr/corr1804.html#abs-1804-02555 https://dblp.uni-trier.de/db/journals/corr/corr1804.html#abs-1804-02555],
: [https://arxiv.org/abs/1804.02555 https://arxiv.org/abs/1804.02555],
: [https://arxiv.org/pdf/1804.02555.pdf https://arxiv.org/pdf/1804.02555.pdf],
: [https://ui.adsabs.harvard.edu/abs/2018arXiv180402555K/abstract https://ui.adsabs.harvard.edu/abs/2018arXiv180402555K/abstract],
: [https://academic.microsoft.com/#/detail/2963860024 https://academic.microsoft.com/#/detail/2963860024]

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Satoh et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 221951367 to Satoh et al 2018a

Scipediacontent: Created page with " == Abstract == Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, t..."