Legacy intelligent transportation systems can often be limited in the visibility they offer to road operators. Aside from the reality that not every area of a road is monitored, cameras are restricted to capturing only what falls within their direct line of sight. Environmental conditions can further reduce visibility and increase risks, potentially leading to dangerous blind spots where accidents may remain undetected, causing delays in emergency response while blocking the road and reducing its efficiency. Response time is particularly essential in critical accidents, where a minute’s difference can mean saving a life.
Lanternn by ValerannTM addresses this challenge with an innovative and patented computer vision technology capable of detecting road events not directly in the vision field of cameras, as well as with events under environmental conditions that would not catch the eyes of a human operator.
Using this technology, combined with LbV’s data fusion engine’s ability to instantly fuse information from hundreds of data points, Valerann empowers roadway operators with 100% monitoring coverage of their roads – displayed on a single pane of glass, equipping operators with tools to make accurate, rapid and data-driven decisions, significantly enhancing road safety, efficiency, and sustainability.
A notable display of this technology recently occurred on a US highway where LbV was deployed. In this instance, a vehicle positioned far from the nearest camera, yet in sight, activated its hazard lights. The visibility was obscured by the distance and foggy weather conditions that typically render such an incident invisible to human monitoring.
LbV was able to identify the flashing lights and issued an alert. Shortly after, a supporting input from a crowd-sourced platform was received, leading to an increase in the severity of the event. Road operators were promptly informed and responded quickly, discovering the alert was indeed signaling a serious accident.
In another case,LbV demonstrated its effectiveness during a night-time incident. A vehicle, positioned just out of a camera's field of view, stopped and activated its hazard lights. Although the vehicle itself was not visible to the camera, a faint reflection of its flashing lights was barely noticeable in the corner of the camera's view. Such a subtle signal, typically overlooked by human operators, was successfully detected by platform’s computer vision algorithm as hazard lights. This prompt identification allowed operators to be quickly alerted, leading to a swift and appropriate response to the incident.
LbV leverages Artificial Intelligence (AI), Machine Learning (ML) and Big Data technologies to consume, analyze and organize millions of data points, from integrated sources, in real-time. The result is a rich, accurate picture of the road, its traffic, infrastructure and conditions, including the most relevant disruptions and safety incidents. Exposed in real-time the fused insights provide focus for road network operators on the highest priority incidents and risks, and the fused context provides the knowledge needed to deploy the most effective response and mitigation plans in any situation. The result is fewer accidents, less traffic disruption, smoother traffic flow, and increased toll revenues.
