Authored by Ashish Raj, COO, Transerve Technologies Pvt. Ltd. Given the dynamic nature of our world today, everything is in a state of constant evolution - especially when it comes to things that are tech-related. Quite habitual to cars that assist us in most aspects of driving, it is absolutely safe to say that a step forward in this evolutionary process is going to be the autonomous car. It is beyond doubt that autonomous driving vehicles are soon going to be a commercial reality.
When you look at a basic yet really crucial aspect for unlocking the full potential of autonomous vehicles, you think of reliable mapping - since the car runs without manual intervention, software, essentially becomes the core component of the car. Simple GPS based maps cannot cut it in this aspect - the next-generation autonomous vehicles will not be able to function on what will then be almost rudimentary technology. Maps for autonomous vehicles need to highly detailed, that can inform the car about every critical road feature, be it the slope and curvature of the road or the markings and any roadside objects. To this end, it is also quite necessary to build maps that can add real-time contextual awareness to the vehicle, so that it can be cognizant of the traffic situation around it.
In essence, the maps developed to facilitate autonomous driving, need to keep the following factors in mind:
When we deal with regular mapping systems, the system can tell us the position of a car most accurately to a meter or so. With HD maps that have been created using deep tech, you can pinpoint the positioning to almost 10 cm. This means that maps need real-time or near-real-time updating of the mapping environment and this can be one through enablers like Cloud-to-Car Mapping Systems and Cars-to-Cloud Mapping Systems.
Hence, deep tech attributes can essentially help the mapping systems for autonomous vehicles in: