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:
• Accuracy in terms of localization of the vehicle with respect to its environment
• Inclusion of various navigation and localization data such as traffic signs, lanes and buildings
• Keep up-to-date with ease and flexibility of critical information and live updates
• Accessibility in terms of availability for all driving conditions and areas that are drivable
• Compact with a capability of efficient data transfer for localization/updates
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:
• Getting real-time updates from other vehicles that would enable better decisions and improving the positioning of the vehicle. Democratized data contribution from vehicles on the road.
• Becoming high-definition, high-precious and 3D with the usage of LiDAR data, panoramic, mobile mapping and other open sources
• Getting micro-accuracy with the help of details of 3D lanes, road borders, guard rails, speed limits, and other traffic restrictions
• Contain the capacity for deep learning algorithms and AI to identify real-time changes
In order to make autonomous vehicles safe as well as efficient, it is not going to be enough to just develop maps using deep tech or a car-to-cloud-to-car mapping system and connect a car to it. Attention needs to be paid to infrastructure software as well that can integrate the navigation system to a common field of information. Armed with cloud-accessible high definition maps and fitted with deep learning algorithms, autonomous vehicles will be able to gather information from satellites, other cars or even the city’s infrastructure.
High definition accurate 3D maps with hyperlocal information and precise navigation system in autonomous cars will change the logistic industry and will shorten the time to deliver goods to the consumer. Logistic industry will no longer have to struggle with maximizing the deliveries in a day and can plan more based on consumer’s convenience. Removing inefficiency from roads will solve the problem of congestion, pollution and contribute to economy by minimizing nearly unproductive travel time.
Hence, a future wherein a smart car will arrive at our doorstep to pick us up and take us to our destination without any driver input, is not that far away. With the help of maps created using deep tech, the cars will be equipped to select the route, set up their speed, leap-frog through traffic and also communicate with other cars, to be able to negotiate interchanges, choose the correct lanes – all the while obeying all the traffic rules and keeping in mind passenger safety.