The integration of Artificial Intelligence (AI) in vehicle perception systems has revolutionised the landscape of autonomous vehicles (AVs). At Provizio, AI is a core component of our innovative 5D Perception technology, enabling on-the-edge object detection, classification, and tracking, as well as enhancing the performance of our sensors. At Autosens this week, our Senior Machine Learning Engineer, Dane Mitrev, took to the stage to present the latest progress on how AI is being leveraged within Provizio. Let’s dive in to some key takeaways below:
At Provizio, we are leveraging AI to deliver on three core goals:
With 5D Perception, AI is utilised in a unique Tri-Level design to deliver compound enhancement from the signal level, through to the point cloud, and finally at the fusion level. In this way, we make the most of our hardware systems by using intelligent software to squeeze out the best possible performance from each layer of the stack. Let’s start with the first layer - point cloud denoising.
The first phase in creating an effective AI model starts with good quality training data. At Provizio, we generate such data using a 3-stage process:
2D Convolutional Neural Networks (CNN): With this method, 3D radar point cloud data is transformed into 2D projections, which simplifies the training process. A unique CNN architecture is then trained on a dataset where noise in the radar data has been identified and labelled. During this process, the CNN learns to identify patterns in the data that represent noise and as a result, once training is complete, the CNN can be used to identify and filter noise from previously unseen data.
In a similar way to how noise patterns are identified and removed using CNNs, patterns that denote real objects can also used to improve the resolution of point cloud outputs. In this case, during the training process, the CNN learns to understand the spatial relationships within high-resolution ground truth point cloud datasets and predict where additional points should be added to increase resolution. Once trained, the CNN can take lower resolution point clouds and enhance them by adding additional points in a way that increases detail and accuracy.
Once the data from our radar sensors is de-noised and enhanced as per the above systems, a further set of neural networks is used to process this data with the goal of understanding the real-world environment it represents. In this respect, our hardware and software teams worked closely together to develop an understanding of how to build a neural network that could extract the most information from the radar point clouds. In doing so, several efficiencies in the process were identified to create a lightweight system, capable of performing advanced perception tasks on-the-edge.
The above provides a high-level outline of the modular process we use in Provizio to maximise the value output of our products. Not only does this approach enable greater maintainability over time, but by developing both the hardware and software for our devices, we posses a unique ability to produce high quality outputs at a fraction of the cost of our competitors. By leveraging AI within our 5D Perception system, we deliver:
The application of AI in vehicle perception is a field rich with innovation and challenges. As AI continues to evolve, Provizio is at the forefront of addressing the complex technical hurdles affecting the safety and real-world variability of autonomous systems, such that a future of zero accidents will become possible for all.
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