NUTONOMY : A CASE STUDY. This is a case study of one of the…
By: Himanshu Yadav
Tags: Autonomous Vehicles, Self Driving Cars, AI, Nutonomy

This is a case study of one of the recently launched vehicle automation company NUTONOMY.

nuTonomy is a developer of state-of-the-art software for self-driving vehicles, developed by two MIT researchers, and first launched in Singapore in 2016.

nuTonomoy’s technology, nuCore allows for flexible and human-like vehicle handling. nuCore’s powerful planning engine enables human-like maneuvering performance, and nuCore’s patented approach to decision making allows autonomous vehicles to handle even the most complex traffic scenarios.

This was about nuTonomy but lets start with the basics, nuTonomy is an example of automation of vehicles using AI. Automation in vehicles has been a hot topic since a long time because whenever there is talk of the future world ,self-driven vehicles are the first scenario. When it comes to autonomy, it might come as a surprise that there are degrees to which a car is autonomous. The International Authority of Automotive Engineers (SAE) has defined six levels of autonomy for self-driving cars.

Level 0 — No Automation

There is zero automation at this level. Majority of the vehicles on road today is manually controlled. There may be systems in place to help the driver, such as the emergency braking system but it doesn’t technically “drive” the car and so doesn’t qualify as automation.

Level 1 — Driver Assistance

These vehicles provide only one automated system for driver assistance, such as adaptive cruise control. The human driver controls the steering and braking with adaptive cruise control.

Level 2 — Partial Driving Automation

This means the vehicle can control both steering and accelerating/decelerating. The human driver can take control of the car at any time.

Level 3 — Conditional Automation

This is where environmental detection capabilities come into play. The car can make informed decisions, such as overtaking past a slow moving vehicle. However, the driver can override the control of the vehicle if the system is unable to execute a task.

Level 4 — High Automation

These cars are fully automated for most circumstances. However, a human driver can manually override the system. nuTonomy comes in this category as most of the nuTonomy vehicles are taxis.

Level 5 — Full Automation

These cars do not require a human driver and don’t even have steering wheels or acceleration/braking pedals.

HOW nuCore works ?

AI use case:

To better understand the role of AI in self-driving cars, let’s first take a look at the human perspective. A human driver uses sensory functions, such as vision and sound to watch the road, road signs, and other vehicles. The years of experience in driving helps the driver develop a habit to look for little things encountered on the roads — it could be a big bump or a pedestrian crossing.

The goal of the automotive industry is to build Level 5 autonomous cars that can drive like experienced human drivers. This means the vehicles need to have sensory functions, cognitive functions and executing capabilities. The process used to achieve this can be divided into three parts.

Part 1: Collection of data from the vehicles

Multiple sensors, cameras, and communication systems are fitted on the autonomous vehicles to generate data of the surrounding environment. This system collects information about everything the autonomous vehicle sees and hears on the road, such as other vehicles, the speed at which every vehicle around it is travelling, road infrastructure, and every object on/near the road. This data is then processed and used to communicate meaningful information (input) to the AI programs.

Part 2: AI Mastermind

The AI programs are stored in the cloud. When the cloud receives the processed data, it starts applying AI algorithms to add meaning to this enormous amount of data. The AI engine is the brain of this entire system and helps in making the right decisions. It is connected to a database that acts as a memory that stores previous driving experiences.

To handle complex situations, nuTonomy uses formal logic, which is based on a hierarchy of rules similar to Asimov’s famous Three Laws of Robotics. Priority is given to rules like “don’t hit pedestrians,” followed by “don’t hit other vehicles,” and “don’t hit objects.” Less weight is assigned to rules like “maintain speed when safe” and “don’t cross the centerline,” and less still to rules like “give a comfortable ride.”nuTonomy car tries to follow all of the rules all the time, but it breaks the less important ones first: If there’s a car idling at the side of the road and partially blocking the lane, nuTonomy’s car can break the centerline rule in order to maintain its speed, swerving around the stopped car just as any driver would. The car uses a planning algorithm called RRT* — pronounced “r-r-t-star” — to evaluate many potential paths based on data from the cameras and other sensors. (The algorithm is a variant of RRT, or rapidly exploring random tree.) A single piece of decision-making software evaluates each of those paths and selects the path that best conforms to the rule hierarchy.

Part 3: Executing the AI Capabilities

Based on the decisions made by the AI engine, the autonomous vehicle knows what to do when it encounters a situation on road and hence it drives through traffic without any human intervention and reaches the destination safely.

Sensors use case :

VEHICLE INCLUDES:

6 electronically scanning radars (ESR)

4 short-range radars (SRR)

4 short-range LiDARs

5 long-range LiDARs

1 trifocal camera

1 traffic light camera

2 GPS antennas

Dedicated Short Range Communications antenna (DSRC)

2 computer and software stacks for safety,Aptiv Connected Services data communications system

Recently, nuTonomy partnered with Lyft to test out vehicles in Boston’s Seaport District, providing rides to Lyft users and gaining more traction toward transforming the way people get around.

That’s it ! Thank you.

#Task_5

Happy learning.