With advancements in artificial intelligence (AI) and 5G network connectivity, smart-road infrastructure technology offers the promise of being added to many different roads, bridges and other transit systems across the U.S. in hopes of improving real-time traffic analytics and tackling the most challenging road safety and traffic management problems.
One technology at the center of this discussion is on the present-day use of AI-enhanced cameras and tomorrow’s promise of LiDAR (light detection and ranging) technology.
AI Will Enhance Camera Sensing Performance
Today, there are hundreds of thousands of traffic cameras deployed in the U.S. alone, and even millions more when CCTV cameras are considered. They are mainly used for road monitoring and basic traffic management applications (e.g., loop emulation).
However, bringing the latest advancements of AI to these assets can immediately improve basic application performance and unlock more advanced software applications and use-cases.
AI and Machine Learning deliver superior sensing performance over traditional computer vision techniques found in legacy cameras. They enable more robust, flexible and accurate detection, tracking and classification of all road users with algorithms that can automatically adapt to various lighting and weather conditions.
In addition, they allow for predictive capabilities to better model road user movements and behaviors, and improve road safety. Agencies can immediately benefit from AI-enhanced cameras with applications such as road conflict detection and analysis, pedestrian crossing prediction and infrastructure sensing for AV deployments.
LiDAR Technology Cannot Fully Replace Cameras
LiDARs can provide complementary and sometimes overlapping value with cameras, however there are still several safety critical edge cases where LiDAR’s technology does not perform well (e.g., heavy rain and snow, granular classification), and where cameras have been proven to handle better.
Dr. Georges Aoude and Karl Jeanbart are co-founders of Derq
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