Can We parallels between Da Vinci’s renaissance & the technological inflection point we are living in today. Technologists are poised to enter a new realm where Operating system (OS) is totally redefining the Industrial revolution process, from computer to smartphone, and now Car Operating system for Autonomous Driving In the era of 4th Industrial Revolution.
Car Operating System is think as system of a systems. A Car Operating System, It’s the brain for the autonomous and Intelligent vehicle, which is capable of interfacing with any automotive standard drive-by-wire platform, and turn it into a driverless unit. It comprises of a highly-robust perception module responsible for sensing and estimation of the environment around the vehicle from vast vocabulary of visual cues (explicit and implicit) such as drivable road space, lane markings, traffic lights, traffic signs, to moving obstacles (both static and dynamic) such as vehicles and pedestrians, all by using camera data. It uses a tactical grade IMU fused with GPS and vehicle Odometry for localization of the vehicle on a max. scale accuracy.
Car Operating System which acts as an human-machine interface between the driver and the vehicle, to self-diagnose repairs and communicate with other vehicles but also manage their internal environment, managing each passenger’s preference for entertainment and work productivity. That is valuable time drivers will re-acquire once relieved of the attention burdens of driving. On the road, everyone will get more hours back in their day to do what they want and not stress out in traffic. The Car Operating System technically turns the vehicle more into an intelligent and connected ecosystem, which is the best example of Human and machine co-work.
With Car Operating System feature a highly intelligent Vehicle Data Record System capable of analysing in-vehicle telematics and repair data in order to predict and prevent problems before they result in mechanical failure. Edge Computing with the Vehicle Diagnostic System running on-board, we would be capable of transforming the entire automotive ownership experience by identifying problems more quickly, reduce unplanned maintenance, improve customer service, lower warranty costs through more accurate diagnoses, and offer diagnostic services tailored to each vehicle/owner.
It’s state-of-the-art AI algorithms can detect driving behaviour patterns from drifting, braking and accelerations that indicate potential danger, coupled with biometric data directly from the driver inside the vehicle, and processes it making required adjustments autonomously in the driving actuation that would help keep it safer or help navigate dangerous driving conditions.
And yes it does integrate with an intelligent Personal Assistant capable of engaging in natural language conversation with the vehicle occupants. From key-less Voice Encrypted Start, to guiding you the best route to your destinations, to playing your preferred music, calling your contacts, to all auxiliary controls it does it all !
Think about the Car Operating System features a high performance microkernel architecture, with an enhanced kernel-level security, encryption, access control and fault tolerance, to make it almost impossible to compromise.
As mentioned, The Car Operating System parts in two modules. One module turns the car into a smart Intelligent car, The other module turns the car driverless. This will also turn the car into a connected Autonomous cars. So, a vehicle forms a part of the traffic cluster and reroute the traffic information to the cloud to update all the other vehicles connected to the cloud network.
Thus Car Operating System is going to suggest you an alternative route when it receives an update that there can be potential delays. Google is already doing this with their Google Maps app, but the connected Car Operating System implementation is going to make it way more accurate than what Google has currently incorporated in their MAPS.
Talking about the V2I ( Vehicle to Infrastructure ), an Car Operating System of self-driving car would be able to get the dwell time left at a traffic light intersection. is going to import the parking information from the nearest parking lot you’re planning to park the car. Automated Self-parking in any kind of indoor/outdoor conditions would be possible for the Self-driving cars.
And talking about V2V (Vehicle to Vehicle) if all the self-driving cars in a traffic network are equipped with Car Operating System and updates the traffic information over the network constantly, the cars become self aware of their neighbouring cars and schedules driving commands to the self-driving-car-computers accordingly and the entire road traffic is going to get intelligent and there might be no more traffic jams in the world.
Latest research work include using state-of-the-art machine learning techniques for modelling an ideal Driving Behaviour where the robotic vehicle doesn’t necessarily drives like a robot but an actual human driver.
Human drivers plan ahead by negotiating with other road users mainly using motion cues – the “desires” of giving-way and taking-way are communicated to other vehicles and pedestrians through steering, braking and acceleration. These “negotiations” take place all the time and are fairly complicated – which is one of the main reasons human drivers take many driving lessons and need an extended period of training until we master the art of driving.
The challenge behind making a robotic system control a car is that for the foreseeable future the “other” road users are likely to be human-driven, therefore in order not to obstruct traffic, the robotic car should display human negotiation skills but at the same time guarantee functional safety yet conform to the driving ethics. Knowing how to do this well is one of the most critical enablers for safe autonomous driving.
The only looming issue with artificial intelligence advancements is no one really knows how the most advanced algorithms do what they do. Deep learning, the most common of all AI approaches, represents a fundamentally different way to program computers.
You can’t just look inside a deep neural network to see how it works. A network’s reasoning is embedded in the behavior of thousands of simulated neurons, arranged into dozens or even hundreds of intricately interconnected layers. The neurons in the first layer each receive an input and then perform a calculation before outputting a new signal. These outputs are fed, in a complex web, to the neurons in the next layer, and so on, until an overall output is produced. Plus, there is a process known as back-propagation that tweaks the calculations of individual neurons in a way that lets the network learn to produce a desired output. Thus the many layers in a deep network enable it to recognize things at different levels of abstraction.
Deep learning has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to make cars drive by themselves and do countless other things to transform whole industries.
But this won’t happen or shouldn’t happen unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur and it’s inevitable they will.
Think about Car Operating System Vehicle Data platform, a block-chain optimized platform that delivers a new paradigm of trust. The platform compliments existing applications and services with three key capabilities – Offers an AI framework with block-chain that is implemented in the vehicle gateway, Facilitates an immutable ledger and Enables multiparty transactions. The result is a revolutionary change in the authenticity of data to and from connected vehicles (V2X).
In my opinion, Autonomous Vehicles also need to define how autonomous systems interact with people, the most difficult parts is making sure the vehicles know what pedestrians are doing and what they’re about to do, pedestrian intent prediction platform makes autonomous vehicles safer and more efficient in real world environments.
Thanks to some incredible breakthroughs in AI Technologies that will give cars a Brain of their own. we still think some key challenges are there, for car learning process with min. data, an neural network framework for sensor fusion confusion on false alarm rate of radar / lidar real world data.
Inspiration from nature, Using ANT COLONY OPTIMIZATION ALGORITHMS Probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs or Using SWARM INTELLIGENCE Figuring out how bats use their biological sonar to move through swarms could someday help us safer self-driving cars, For example echolocation to improve other sonar and radar devices.
I’m really looking forward to a time when generations after us look back and say how ridiculous it was that humans were driving cars. – Fisheyebox – Intelligence Innovatively Engineered.