Safety as a System:
Urban road safety depends on more than individual alerts or standalone applications. It requires connected systems that enable vehicles, vulnerable road users, and other mobility methods to share accurate information in real time.
The below scenarios are illustrations created by our outstanding partners at Software République, depicting common and high-risk situations in dense urban traffic, from buses leaving public stops to cyclists navigating mixed mobility environments.*
Where Does Eye-Net Come in?
Across these scenarios, Eye-Net Mobile plays a dual role. In high-risk situations such as side collisions, Eye-Net’s SDK is directly involved in detecting danger and triggering emergency alerts. In other scenarios, Eye-Net operates as a background data layer, enabling reliable, low-latency data distribution that allows awareness notifications and safety alerts to function at scale.
Together, these illustrations show how V2X safety moves from concept to reality not as a single feature, but as a system-level capability embedded into the urban mobility ecosystem.
Scenario #1: Side Collision Alert (direct Eye-Net involvement)
Side collisions are among the most dangerous urban scenarios, often happening at intersections or blind spots where road users cannot see each other in time. In this scenario, Eye-Net Mobile’s SDK is directly involved, detecting high-risk trajectories in real time and delivering emergency alerts to vehicles, cyclists, and pedestrians. The system operates beyond line of sight, enabling collision prevention precisely where traditional sensors and human reaction fall short.
Scenario #2: Risk of Hitting a Cyclist When a Bus Leaves a Public Stop
When a bus merges back into traffic after stopping, cyclists overtaking from behind are exposed to sudden and severe risk. In this scenario, Eye-Net’s accurate real-time data distribution runs in the background, ensuring that all participants share a reliable and synchronized view of the situation. This enables timely alerts to be issued by the mobility ecosystem and significantly reduces the risk of dangerous merge-out collisions.
Scenario #3: Bus Presence Behind
Cyclists are often unaware of large vehicles approaching from behind, especially in noisy or congested environments. In this scenario, Eye-Net operates in the background, ensuring that bus presence data is accurately shared in real time. This allows awareness alerts to reach cyclists early enough to adjust position or behavior and ride more safely.
Scenario #4: Bus Overtaking
When buses overtake cyclists, small misjudgments in speed or distance can have serious consequences. Eye-Net’s technology enables this scenario by maintaining low-latency, high-confidence data exchange between vehicles and vulnerable road users. The result is earlier awareness and smoother, safer overtaking behavior in dense urban traffic.
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Additional Scenarios: Longitudinal Collision Awareness (Vehicle–Cyclist)
In longitudinal scenarios, where vehicles approach cyclists from behind, Eye-Net supports the ecosystem by ensuring that position, speed, and direction data is continuously synchronized. This background capability enables awareness alerts that give both drivers and cyclists the time needed to react safely, particularly on shared or narrow roadways.
Conclusion
Taken together, these scenarios illustrate a powerful idea: by combining direct, real-time collision prevention with a reliable background data layer, Eye-Net Mobile enables cities and mobility operators to move from reactive safety measures to proactive, connected protection.
Whether actively preventing side collisions or quietly ensuring that critical data flows between road users, Eye-Net turns V2X safety into an infrastructure capability, one that scales across transport modes, environments, and urban realities.
*Some scenarios are illustrated in multiple alert configurations across different user interfaces.
