en
Please fill in your name
Mobile phone format error
Please enter the telephone
Please enter your company name
Please enter your company email
Please enter the data requirement
Successful submission! Thank you for your support.
Format error, Please fill in again
Confirm
The data requirement cannot be less than 5 words and cannot be pure numbers
https://www.datatang.com/
https://www.datatang.ai/
m.datatang.ai
From:Datatang Date:2023-08-11
The emergence of self-driving cars is met with anticipation and expectation. The convenience of being able to engage in various activities while the vehicle autonomously navigates the road is appealing. Although the technology is still in its early stages, it's increasingly becoming a standard feature, particularly in the form of assisted driving systems that prioritize driver awareness.
These systems, designed to monitor driver behavior, have demonstrated their effectiveness in enhancing road safety. For instance, a prominent company reported a substantial reduction in accidents attributable to driver negligence over a single quarter, all thanks to the implementation of self-driving assistance features.
To optimize these features, high-quality training data is essential, ensuring the system's accuracy in judging cues and detecting driver inattention. Inadequate data quality would compromise the desired results, leading to increased accidents on the road.
So, how does this function work? The monitoring system captures driver behavior in real-time, analyzing it through machine learning algorithms to determine if attention is focused on the road or if distraction is present. Traditional infrared cameras capture this information, functioning effectively in various lighting conditions. The system primarily targets distracted behaviors such as phone use, fatigue, and conversations while driving, alerting the driver through vibrations or noises to regain focus.
Consider a scenario where an individual, exhausted from a long day's work, must drive home. Assisted driving comes to the rescue, detecting signs of fatigue through the camera and alerting the driver to take a break, significantly reducing the risk of fatigue-induced accidents.
However, distractions remain a key concern, such as phone calls, chatting, or emotional distress, which can also contribute to accidents.
At Datatang, we offer cutting-edge solutions to support the development of these technologies. Our "Human-in-the-loop" intelligent AI data annotation services enhance labeling pipelines, improving efficiency up to 3-4 times. Our platform includes 28 annotation templates and automatic labeling tools, catering to diverse annotation needs.
We prioritize data security, implementing comprehensive compliance measures across our AI data collection, custom services, and annotation platforms, safeguarding our customers' rights and interests. With Datatang, our customers can confidently leverage our AI data services.
Our data collection and annotation services stand ready to provide high-quality training data, empowering customers to deploy AI models more efficiently, advancing the future of autonomous driving technology.
We use cookies to enhance your browsing experience, serve personalized ads or content, and analyze our traffic. By clicking "Accept All", you consent to our use of cookies.