Road accidents and risk prevention: Using Lanternn by Valerann™ for making roads safer


Most new vehicles today – whether commercial or private – are fitted with advanced vision sensors that aid with areas such as hazard detection and driver assists. In this case study, we look at how Valerann® has been able to use vehicle vision systems as a data source for Lanternn by Valerann™ and with AI-powered analytics has been able to identify temporary but highly risky events taking place along highways, including a real-world example on one of Israel’s biggest highways.


The Ayalon Highway in Tel Aviv, Israel, hosts a quarter of a million journeys daily. As many as 1,000 events occur on the road in a single day. Ayalon historically found it difficult to deal with this volume of incidents; they were forced to prioritize only the most important events. However, with recent developments in technology, new data sources became available that presented opportunity to improve Ayalon’s capability: specifically, data from dash cams can facilitate improved road monitoring and automation. Ayalon required support from Valerann to harness this new data source effectively to increase safety on the road.


Valerann was able to obtain real-time GDPR-compliant access to dashcam footage of drivers using the Ayalon Highway. These AI-equipped dash cams automatically send Valerann information on anomalies occurring on the road: a stationary car, a pothole, or a pedestrian spotted on the road.

After analyzing the data using its advanced proprietary Artificial Intelligence (AI) and Machine Learning (ML) algorithms, Valerann identified that pedestrians were habitually moving across the highway at key locations, putting themselves at risk of collision. To find out more, Valerann used its LbV data fusion engine to map all pedestrian crossing hotspots. This map was cross correlated with Valerann’s Machine Vision (MV) and video processing capabilities and other data sources – including Internet of Things(IoT) sensors, connected vehicle data, stationary cameras, navigation apps – to determine what was actually happening.

The analysis revealed that people were using motorway exits as a shortcut to their destination. Cars were dropping pedestrians off at certain exit slip roads, where the passengers would jump over the safety barrier. These temporary but highly risky behaviors were not picked up by normal sources of road monitoring because they happened too quickly. The analysis also showed that dash cam footage could be useful for validating accidents reported on Waze or determining their cause.


Once equipped with this detailed information and enhanced analytical capability, Ayalon Highway was empowered to take preventative measures including:

·      Putting up warning signs to deter risky actions

·      Placing a camera at the hotspots to monitor events further

·      Strategically placing patrols

·      Blocking the shortcut with a fence placed behind the safety barrier.

These actions helped to remove the safety risk altogether and significantly increase safety.