Map, Detect, Ensure
Airports are intricate environments requiring precise monitoring to ensure the safety and efficiency of aircraft operations and passenger movement. Traditional surveillance systems often struggle to provide accurate, real-time data across expansive and dynamic airport settings. LiDAR (Light Detection and Ranging) technology addresses these challenges by providing high-resolution, three-dimensional mapping capabilities, which enable airports to enhance operational efficiency and safety.
A Unique Challenge
LiDAR-based models of the airport and its surroundings offer precise topographical information, facilitating better planning for developing infrastructure, such as runways, taxiways, and terminal buildings. By accurately mapping terrain and identifying potential obstacles, LiDAR helps mitigate risks associated with bird strikes, debris on the runway, inclement weather, and other hazards. LiDAR technology also plays a crucial role in monitoring passenger flow within airport terminals. By anonymously tracking individuals' movements, LiDAR systems provide continuous, 24/7 monitoring capabilities, offering valuable data on occupancy and foot traffic while preserving privacy. This information enables airport operators to allocate resources more effectively, reduce congestion, and enhance the overall passenger experience.
The Airport Monitor
In the case of The Indoor Lab, Innoviz Technologies' InnovizTwo LiDAR sensors are being installed at major U.S. airports, including Denver, Tampa, and Nashville International Airports. This deployment aims to enhance passenger safety and operational efficiency by providing precise, real-time data through advanced 3D sensing and AI capabilities. The data gathered from the LiDAR, point cloud, and analytics are used to optimize traffic design and flow while promoting pedestrian, automobile, and other vulnerable road user safety. This is done by intelligent data collection of data for root cause analysis of pedestrian-related incidents (or near-miss collisions) through and predictive analytics.