Understand the opportunities of AI cameras and LiDARs for smart road infrastructure

With advances in artificial intelligence (AI) and 5G network connectivity, smart road infrastructure technology holds the promise of being added to many roads, bridges and other public transit systems across the United States. United.

Understand the opportunities of AI cameras and LiDARs for smart road infrastructure

Dr. Georges Aoude is the co-founder | derq

While January’s Consumer Electronics Show (CES) sparked a new wave of autonomous vehicles (AVs) in the automotive market over the next few years, the focus of late has been on the technology in these vehicles themselves. However, technology embedded in road infrastructure is also starting to be discussed more between service providers and municipalities.

With advances in artificial intelligence (AI) and 5G network connectivity, smart road infrastructure technology holds the promise of being added to many roads, bridges and other public transit systems across the United States. United in the hope of improving real-time traffic analysis and tackling the problem. the most difficult problems of road safety and traffic management. A technology at the center of this discussion concerns the current use of AI-enhanced cameras and the future promise of LiDAR technology.

Artificial intelligence will improve the detection performance of the camera

Today, hundreds of thousands of traffic cameras are deployed in the United States alone, and even millions more when CCTV cameras are taken into account. They are mainly used for road monitoring and basic traffic management applications (eg loop emulation). However, bringing the latest advancements in AI to these assets can immediately improve the performance of basic applications and unlock more advanced software applications and use cases.

AI and machine learning provide superior detection performance over traditional computer vision techniques found in older 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 offer 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 detection for AV deployments.

LiDAR technology cannot completely replace cameras

LiDARs can provide complementary and sometimes overlapping value with cameras, but there are still several safety-critical cases where LiDAR technology does not perform well (eg, heavy rain and snow, granular classification), and where it has been proven that cameras handle better. Additionally, today’s LiDAR technology remains expensive to deploy on a large scale due to its high unit price and limited field of view. As an example, it would take multiple LiDARs at considerable investment to deploy in a single intersection, where a single 360-degree AI camera may be a more cost-effective solution.

For many budget-minded communities, AI cameras remain the proven technology of choice today. Over time, as the cost of LiDAR technology moderates, communities should evaluate increasing their infrastructure with such sensors.

Eventually, sensor fusion will give good results

When the cost of LiDAR technology finally sees an early reduction, it will be seen as a solid and viable addition to the AI-enhanced cameras that are installed today. Similar to autonomous vehicles, sensor fusion would be the go-to approach for smart infrastructure solutions and would maximize the benefits of both technologies.

Comparison of relative camera performance against LiDARs today










Feature

Legacy camera

AI-powered camera 1

LiDAR

AI-powered camera and LiDAR fusion

Difficult lighting (low light, glare)

Low

Medium

High

High

Adverse weather conditions (snow, rain, fog)

Low

High

Medium

High

Location

Low

Medium

High

High

Classification

Low

High

Medium

High

Affordability

High

Medium

Low2

Low2

  1. Assumes the presence of an IR or a good low-light sensor
  2. Should improve over time

The use of an AI-powered camera that is economical and capable today, combined with the great potential of LiDAR in the years to come could help communities and municipalities achieve a win-win scenario today and tomorrow.

Ultimately, the goal is clear to improve overall traffic flow and reduce vehicle accidents and fatalities, but the technology and implementation strategy must be appropriate to achieve this. The technology for monitoring our roads must also change, which today calls for consideration of AI-powered cameras with the promise of LiDAR tomorrow.

About Dr. Georges Aoude

Dr. Georges Aoude is the co-founder of Derq, an MIT spin-off that is powering the future of connected and autonomous roads, making cities smarter and safer for all road users, and enabling the deployment of vehicles autonomous on a large scale. Derq provides cities and fleets with an award-winning, patented AI-powered smart infrastructure platform that helps solve the toughest road safety and traffic management challenges.

The content and opinions of this article are those of the author and do not necessarily represent the views of RoboticsTomorrow

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