Lidar operates on a laser beam rapidly scanned in 3D space. Its output is a 3D point cloud indicating distances to objects the beam touches. Point clouds can be highly detailed but difficult to interpret, especially as a vehicle and surrounding objects move.
Advanced modeling can find scan-to-scan differences, such as determining when another vehicle has pulled alongside, or is in a blind spot, or is straying from its lane. Lidar has one other advantage - it can sense height, including clearances to bridges and parking structures, essential for high-profile vehicles.
Much of the research into AI inference is focused on analyzing these point clouds, since many types of information can be harvested from them. AI inference techniques such as open-set semantic segmentation may help with unstructured lidar data.
Cameras are good at replicating many of the features of human vision. Increasing frame rates and pixel resolution produces higher quality images. But, these increases introduce a challenge: more frames and pixels mean more processing.
Increased detail means an object in the distance may be detected more quickly. This may buy critical time, especially for a heavy truck at highway speeds which requires more distance to maneuver and stop. Accurate detection may mean every pixel in a scene needs semantic segmentation with a label.
AI can give cameras more capability such as real-time depth estimation, crucial for camera-only systems in self-driving vehicles to perceive correctly. Attention-based AI models may also improve detection of objects in camera images.
Making vehicles smarter with AI inference is only part of the change needed to help roads be safer and more efficient. Another initiative is intelligent transportation, adding features to roads for gathering real-time data and assisting drivers.
Vehicle counting has existed for years, but now much more information can be extracted from cameras and other sensors. Vehicle types can be identified, as well as pedestrians and bicycles. Vehicle spacing can be monitored and lane changes can be observed. Emergency vehicles can be notified and guided to incidents.
Vehicle-to-everything (V2X) systems also come into play. V2X can help enforce vehicle speeds and spacing. Intelligent transportation may also eventually coordinate traffic on roads with other modalities such as light rail and air taxis.
Given the tectonic shift in information processing at the edge, companies are now seeking near cloud level performance where data curation and AI driven decision making can happen together. Due to this shift, the market opportunity for the EdgeCortix solutions set is massive, driven by the practical business need across multiple sectors which require both low power and cost-efficient intelligent solutions. Given the exponential global growth in both data and devices, I am eager to support EdgeCortix in their endeavor to transform the edge AI market with an industry-leading IP portfolio that can deliver performance with orders of magnitude better energy efficiency and a lower total cost of ownership than existing solutions."
