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smart-driver-assistant/3 (latest)
Smart Driver Assistant Computer Vision Project
datasets
Updated 2 years ago
Metrics
mAP is equal to the average of the Average Precision metric across
all
classes in a
model.
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mAP
Precision measures how often your model's predictions are correct.
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Precision
Recall measures what percentage of relevant labels were successfully
identified.
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Recall
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Description
Here are a few use cases for this project:
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Accident Prevention: "Smart Driver Assistant" can be used to identify if a vehicle deviates from its lane which can potentially help to prevent accidents and enhance safety on roads.
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Advanced Driver-Assistance Systems (ADAS): It can be incorporated in the ADAS of modern vehicles, notifying the driver in case of any lane departure to reduce human error.
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Predictive Maintenance: This model can be used in monitoring the car's driving patterns, detecting unexpected lane-departure, and attributing it to potential vehicle malfunctions, helping to schedule appropriate maintenance.
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Driving Behavior Analysis: Insurance companies could use it to analyze driving behavior for personalized insurance policies based on risk factors, such as repeated lane-departure incidents.
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Autonomous Vehicles: The model can be an integral part of self-driving car technology to efficiently manage lane departures and drives in reverse, improving their reliability and safety.
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Cite This Project
LICENSE
CC BY 4.0
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
smart-driver-assistant_dataset,
title = { Smart Driver Assistant Dataset },
type = { Open Source Dataset },
author = { datasets },
howpublished = { \url{ https://universe.roboflow.com/datasets-cjfc2/smart-driver-assistant } },
url = { https://universe.roboflow.com/datasets-cjfc2/smart-driver-assistant },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { mar },
note = { visited on 2025-04-03 },
}
