Rahio | Making Future Autonomous


ADAS Advanced Driver Assistance System, to help the driver in the driving process. Safety features are designed to avoid collisions and accidents by offering the AI technology that alert the driver from potential problems and warn them to take measures before dangers happens.

Advanced Driver Assistance Systems (ADAS) frameworks extend on the range of unresistant/dynamic:

A unresistant framework also know as passive framework, alarms the driver of a possible threatening circumstance so the driver can make a move to address it. For instance, Lane Departure Warning (LDW) alarms the driver of unintentional /accidental lane departure; Forward Collision Warning (FCW) shows that under the current dynamics comparative with the vehicle ahead, a crash is unavoidable. The driver then needs to slow down so as to maintain a strategic distance from the impact.

Interestingly, dynamic security frameworks make a move. Automatic Emergency Braking (AEB) distinguishes the impending impact and brakes with no driver intercession. Different instances of dynamic obligations are Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), Lane Centering (LC), and Traffic Jam Assist (TJA).

Rahio Driver-Assistance Systems currently using radar technology to provide parking assistance, collision avoidance and other driver aids.



Rahio produces programming that conducts sensor combination – gathering information from camera sensors just as radar and LiDAR sensors. We utilize a creative and restrictive arrangement of calculations to recognize objects and the sheltered way ahead. This Environmental Model is the essential wellspring of data to help the framework’s dynamic.


Autonomous vehicles will require numerous framework redundancies to manage unanticipated conditions. A superior quality guide is required for exact confinement of the vehicle, comparative with street limits and crossing points, under all conditions.

Driving Policy

When autonomous vehicles can detect the scene around it restrict itself on a map, the last piece permitting it to impart the way to human drivers is driving policy. Sensing, mapping and ground-breaking registering stages give the vehicle super-human sight and response times. Rahio’s reinforcement learning for driving policy will give the human-like instinct and conduct required to examine multivariable circumstances and haggle with human drivers.