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---
tags:
- time series
- time series classification
- monster
- other
- sensor
license: other
pretty_name: FordChallenge
size_categories:
- 10K<n<100K
---
Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
|FordChallenge||
|-|-:|
|Category|Sensor|
|Num. Examples|36,257|
|Num. Channels|30|
|Length|40|
|Sampling Freq.|10 Hz|
|Num. Classes|2|
|License|Other|
|Citations|[1]|
***FordChallenge*** is obtained from Kaggle and consists of data from 600 real-time driving sessions, each lasting approximately 2 minutes and sampled at 100ms intervals [1] (i.e., a sampling rate of 10 Hz). The processed dataset consists of 36,257 multivariate time series each of length 40 (i.e., representing 4 seconds of data per time series at 10 Hz). These sessions include trials from 100 drivers of varying ages and genders. The dataset contains 8 physiological, 11 environmental, and 11 vehicular measurements, with specific details such as names and units undisclosed by the challenge organizers. Each data point is labeled with a binary outcome: 0 for "distracted" and 1 for "alert". The objective of the challenge is to design a classifier capable of accurately predicting driver alertness using the provided physiological, environmental, and vehicular data.
[1] Mahmoud Abou-Nasr. (2011). Stay Alert! The Ford Challenge. <https://kaggle.com/competitions/stayalert>. Kaggle. |