An autonomous driving technology company in the US wanted to optimize their vehicle performance at an optimal level and the best way they could find through digitizing their operation patterns using autonomous technology. However, they looked beyond typical Automated Driving Assistance System or ADAS technology. They wanted their vehicles to drive the longest miles on the road. They thought it could help them heighten the driving experience by simulating data while also focusing on building cost-effective and efficient transportation for their business growth.
In their mission, as they joined forces with SYNERGYLABS, our synthetic datasets efficiently trained AI models to help their vehicles achieve accuracy and enhance autonomous driving in real-world scenarios.
About the autonomous driving technology company
The client established a trusted autonomous driving technology company in North Carolina, US. With more than 2 years into the operations of building autonomous driving technology, they have clients from top locations in the US, and they are home to more than 10k reliable clients. Their technologies are highly preferred for business across retails, manufacturing, fashion, and more. They are growing from a team of ten members to 80 members now with the increasing operating pressure, which needs to ensure high performance and efficiency for their users. The ease of use is highly expected to make returning to the parking lot or safety easily achievable. The regular market demand for products urges them to implement autonomous driving technology for efficiency, lower risk of collisions, and high performance for their users.
Autonomous vehicles are doing rounds for a long time now with AI-based drones delivering cargo at doorstep or ride-hailing services making riding more efficient and easier for users. Although LiDAR or RADAR sensors make building autonomous vehicles apparently purposeful, a deep level application of these technologies proves effective when they help with simulation and encourage a safer and more productive future for riders any autonomous vehicle companies.
The client understood the pressing needs of building algorithms using massive datasets or by generating synthetic data in the real world. They needed these massive synthetic datasets to help with training models that blend closely with related scenarios and enable friction-free autonomous driving.
The purpose behind harnessing synthetic data and using them to build AI models was to detect real-world situations in real-time more accurately. They seek the level of accuracy or expertise to generate synthetic data and integrate it into the ADAS technology so as to nurture their autonomous vehicle goals more effectively.
As ADAS and its features also aim at making autonomous driving environments easier and efficient, they still lack capabilities to offer accuracy and precision with data detection.
To help with harvesting data and building synthetic datasets, it needs extensive expertise and experience across the Artificial Intelligence, that supports the development of AI models to be properly trained and programmed to aid in autonomous driving solutions. They wanted to collaborate with leading AI consulting partner with strong industry experience and a detailed orientation to AI tools and technologies. They turned to SYNERGYLABS to help them train modern deep neural networks using synthetic data and build advanced AI data models to work in the most critical road situations.
SYNERGYLABS’ built synthetic data provided the best template to build AI-based autonomous security systems that easily sync with ADAS tools and most significant features like radar sensors, LiDAR units, and cameras.
Accurate detection of different real-world scenarios
Our synthetic data-based platform is programmed to simulate off-road and on-road traffic scenarios. Additionally, pixel-perfect annotated training data is capable of delivering accuracy through simulation of multiple scenarios and empowers their vehicles to be able to identify and overcome different perception issues. The autonomous capability that we built for their vehicles through synthetic data generation gives their drivers the ability to drive safely by avoiding the toughest terrain, having better cruise control with accurate path and space judgment, and also escaping blind spots.
Increasing the ability to be trained with synthetic data
By helping train the decision-making and recognition algorithms of their AI models to be built into autonomous technologies like ADAS or other features, we prepare accurate datasets. The raw images the vehicles capture pertain to different sets of objects like traffic lights, moving vehicles, people, and road signs are some of the critical things the AV needs to recognize. We harness raw data and use bounding boxes and labels to infuse them back to AI models. By analyzing and detecting thousands of raw images and other key data, AV gains insights into recognizing the objects and improves comprehension to respond accurately to the evolving scenarios.
As they used synthetic data for AI-models, they were able to maximize the power of autonomous vehicles and developed unique features to detect the drivable path or passable area without creating any threat opportunity for the nearby objects like cyclists, pedestrians, or other vehicles.
A high-quality data annotation makes it easier for them to train their AI models and encourages the development of safer autonomous vehicles.
We generate synthetic data to recognize billions of data around the driving pathway. They include-
Our end-to-end synthetic data platform provides better simulation for ADAS technologies, which elevated our clients’ expectations to build a safer and cost-effective driving experience for users. From training to testing to deployment, our AI-powered technologies delivered the best results with precision, which made their investment into synthetic data generation purposeful and efficient at the same time.
Computer vision, AI, synthetic data, Ext JS, HTML, Java, Springboot, MySQL, Postgres, Socket programming with netty and Tensor Flow, python.