4500339725center290090900UNIVERSITY OF MAURITIUS
NAGA Homush Dev, 1780061
19 April 2018
As part of fulfilment of the Course Programme
Bsc (Hons) Information Technology Diploma to Degree Top-up
Current Trend in IT and Computing MIBS S4042(5)
NAGA Homush Dev, 1780061
19 April 2018
As part of fulfilment of the Course Programme
Bsc (Hons) Information Technology Diploma to Degree Top-up
Current Trend in IT and Computing MIBS S4042(5)

TOC o “1-3” h z u 1.Technical Detail PAGEREF _Toc514533478 h 31.1How the car works? PAGEREF _Toc514533479 h 42.Current Trend PAGEREF _Toc514533480 h 52.1Tesla PAGEREF _Toc514533481 h 52.2NVIDIA and Volkswagen PAGEREF _Toc514533482 h 63.Critical Analysis PAGEREF _Toc514533483 h 84.Future applications PAGEREF _Toc514533484 h 85.Innovativeness and creativity PAGEREF _Toc514533485 h 96.REFERENCES: PAGEREF _Toc514533486 h 11
Technical DetailWith the advance of technology, Artificial Intelligence (AI) slowly but surely is making its way in the field of computer science. Machines are being inverted that can imitate human intelligence. Merrian-Webster has defined artificial intelligence as the simulation of intelligent behaviour in computer to imitate intelligent human behaviour which is dealt by the department of computer science (Marr 2018).
Investment in AI development is normally done in order to:
Build systems that think alike like humans do(“strong AI”)
Get systems to work without figuring out how human reasoning works(“weak AI”)
Build a model of human reasoning to carry out experiences
The transportation industry has made a step forward by applying artificial intelligence in mission-critical tasks such as manufacturing self-driving passenger carrying vehicles. However, questions are being asked in the reliability and safety of such AI system by the general public. The self-driving car project was lain the first stone by Google in 2009 and since that time, testing has been carried out in its prototype car on the US roads and details about the functioning of the self-driving cars were analysed. It was in 2016 that Waymo, the fully automated self-driving car of Google, was set for its test-driving.

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Waymo has eight sensors installed in it. The eye-catching feature is the rotating roof-top known as LiDAR which is a camera using an array of 32 or 64 laser for measuring distance from the sensor to objects to build up a 3D map at a range of 200m, hence letting the car “see” hazards.

The car has a rear-mounted aerial installed externally and an ultrasonic sensor on one of the rear wheels. The former receives geolocation information from GPS satellites while the latter is used for the monitoring of the car’s movements.

For finer measurements on the car’s position, altimeters, gyroscopes and a tachometer (a rev counter) are all equipped in Waymo. These combination results in highly accurate data needed to operate the car safely.

Figure SEQ Figure * ARABIC 1 Anatomy of an Autonomous Car
How the car works?Ideally, a single sensor is not responsible for the good functioning of Waymo. For example, GPS data is not accurate enough to keep the car on the correct track. Instead, the driverless car uses the combined data from all eight sensors interpreted by Google’s software, to get the car travelled from place A to place B safely.

The data received by the Google’s software is used commonly to identify the highway signals within the car positional region and also to identify accurately other road users and monitor their behaviour patterns. For example, Waymo can successfully identify a bike and understand that if the cyclist extends an arm, they intend to make a manoeuvre. The car then knows to slow down and give the bike enough space to operate safely (Woollaston 2016).

Current Trend
Tesla is considered as one of the biggest car manufacturer using Artificial Intelligence. According to Elon Musk, The CEO of Tesla, the company is planning to support its future generation of self-driving technology by developing its own custom artificial intelligence (AI) chip.

Major advancements have been achieved on Tesla Vision suite and Autopilot feature for its current Model S and Model X fleet and also in Tesla’s Semi truck that will include Autopilot technology which is pushing the industry forward on building fully autonomous vehicles. With the ongoing development of an AI chip in house, this gives the company an edge over competitors in the race of manufacturing of self-driving cars.

Nvidia’s hardware is the current tool that Tesla uses to power its driving-assist features. With the company’s plans to activate the Full Self-Driving capabilities, it will bring a promising option for the future Model S and Model X buyers to navigate a vehicle without human intervention.

Moreover, all Tesla cars manufactured after October 2014 had already been using radar for autonomous driving which relied more heavily on the car’s optical camera. Since then, Tesla announced the latest software update for tits self-driving cars which will be relying on radar unlike optical camera to make decisions. This has been possible due to the update to Version 8.0.
With the use of radar, by sending radar pulses at constant frequency around the car and detecting the time for the echo to return, distance from any features and past vehicles in its path can be calculated and necessary action can be taken by the system of the car. So even if a car in front of a Tesla car doesn’t notice an obstruction or brakes late or drives right into it, the trailing Tesla should be able to brake ahead of time and avoid both.

Similar to any form of artificial intelligence, Tesla’s fleet will only enjoy its expansion with the increase in its number of users. Currently, there are around 100,000 Tesla car on the road using this radar technology but it is only the beginning of Tesla autonomous driving. With more and more of its vehicles on road, this will create the safest cars’ AI since it will help establishing Tesla’s maps with its fleet information of almost near certainty what does and does not constitute as an obstruction in the road (Kevin 2016).

NVIDIA and VolkswagenNVIDIA will work in partnership with Volkswagen as the graphics chipmaker’s artificial intelligence platforms with aims to bring improvement in the autonomous vehicle industry. With their vision of combining AI and deep learning in developing the future generation of intelligent Volkswagen vehicles by using the NVIDIA DRIVE™ IX platform, it will create new cockpit experiences and hence will improve safety.

NVIDIA provide NVIDIA DRIVE as chipmaker which is an AI platform to accelerate production of automated and autonomous vehicles. This platform allows to build and deploy self-driving cars, trucks and shuttle that are functionally safe and can be certified to international safety standards. The NVIDIA DRIVE platform also combines deep learning, sensor fusion, and surround vision to bring a change in the driving experience. It has the capacity of understanding the real-time environment around the vehicle thus precisely locating itself on an HD map and setting a safe path forward for the car. Having been designed around a diverse and redundant system architecture, the platform is built to support ASIL-D, the highest level of automotive functional safety (Sage 2018).

Deep learning which can be applied with AI and machine learning uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others.

Figure SEQ Figure * ARABIC 2 Deep Learning
The NVIDIA DRIVE IX software development kit (SDK) enables AI assistants for both drivers and passengers, using sensors inside and outside the car. DRIVE IX leverages data from the microphone and cameras to track the environment around the driver. Even when the car isn’t driving itself, it is looking out for the driver.

Figure SEQ Figure * ARABIC 3 NVIDIA DRIVE IX Software
NVIDIA offers an end-to-end mapping technology for self-driving cars, designed to help automakers, map companies, and startups rapidly create HD maps and keep them updated. This state-of-the-art technology uses an NVIDIA AI supercomputer in the car, coupled with NVIDIA Tesla GPUs in the data centre, to create highly detailed maps.

Figure SEQ Figure * ARABIC 4 End-to-End HD Mapping
Critical AnalysisSelf-driving cars will potentially have a huge positive impact on our safety and lifestyle. The high-tech vision system has the potential to outperform humans in detecting dangerous situations. Unlike distracted or drunk drivers, self-driving cars always operate at their maximum ability.
Therefore, they are likely to reduce accidents and lower the huge death toll on our roads. Additionally, the smart algorithms will find faster routes to our destinations, drive more efficiently and consume less fuel. We will have the added benefit of spending our time on other tasks while our car is driving for us.

As for navigating down a road, the self-driving car requires very precise maps of the street and surroundings. These need to be much more detailed than those found on common online map services and will require more time to be created for every road in the country or the world. Moreover, more work needs to be done on the computer algorithms that constitute the car’s “brain.”
Software engineers do not yet trust their programs to correctly assess all the possible situations that can occur in traffic. In addition, more data needs to be collected to reliably train the machine learning algorithms. Therefore, testing of self-driving prototypes is still closely supervised. A safety driver is present at all times, ready to intervene if the car makes a mistake (Menke 2017).
Future applicationsNVIDIA CEO Jensen Juang made two self-driving announcements during his keynote. The first one was made with Deutsche Post DHL Group (DPDHL), the world’s biggest mail and logistics company, and ZF, an automotive provider. The three companies have teamed up to deploy a test fleet of autonomous delivery.

DPDHL will deploy electric light trucks powered by the ZF ProAI self-driving system, which is powered by the NVIDIA DRIVE PX palm-size supercomputer but also includes sensors, cameras, LIDAR and radar that feed the data into the system. The AI-based system will automate the process of transporting and delivering packages from the central point to the final destination, which is the most expensive aspect of courier services. The autonomous vehicles will be able to use the AI to comprehend the environment it is in, then plan a safe path and park itself. It ensures safe, accurate deliveries at a significantly lower cost than using people (Kerravala 2017).

Innovativeness and creativityBy 2030, completely autonomous vehicles will be pervasive and will transform our cities and lifestyles. They’ll change the nature of businesses, reshape the urban landscapes, affect which suburbs we choose to live in, and help us to save time and money. Even better, they could substantially improve our health and wellbeing (Bohan 2017).

No More Traffic, No More Carparks

AI applications are likely to transform transportation toward self-driving vehicles with on-time pickup and delivery of people and packages. There will be fewer cars being put to much more efficient use. Factor in the networked capabilities of the cars, which will know the best routes in advance, in combination with the growing trend of working from home, and the peak hour rush really could start to become a thing of the past.

Fewer cars and less idle time also means less parking space will be needed. A lot less! It also means no more time wasted circling around backstreets and parking lots to find a space. The car will simply drop you off and go. You get to save the money you would have spent on parking and avoid the stress of finding somewhere to leave your car.

More Housing, More Parks

According to the Stanford report “As cars will become better drivers than people, city-dwellers will own fewer cars, live further from work, and spend time differently, leading to an entirely new urban organization.”
Car companies like Volkswagen, Mercedes, Toyota and General Motors agree, and they are swiftly transforming their business models on the basis of this prediction. Specifically, they are investing in artificial intelligence technologies and buying up, or partnering with, ride-sharing services like Lyft and Uber.

More Time

Self-driving cars can facilitate increased comfort and decreased cognitive load. Whether it is work, meditation, or kicking back and watching videos, you can get a lot more done when you’re not spending an hour a day piloting a fast moving vehicle. In addition, autonomous vehicles could eliminate a lot of time currently spent on shopping and errands.

REFERENCES:BOHAN, E., 2017. How Self-Driving Cars Will Transform Urban Living for the Better. Big Think online. Available from:
http://bigthink.com/articles/how-self-driving-cars-will-transform-urban-living-for-the-better Accessed on 18 May 2018.

KERRAVALA, Z., 2017.The future of self-driving cars: New tech advances possibilities. NETWORK INTELLIGENCE online. Available from:
https://www.networkworld.com/article/3231222/internet-of-things/the-future-of-self-driving-cars-new-tech-advances-possibilities.html Accessed on 12 May 2018.

KEVIN, J. R., 2016. Tesla Explains How A.I. Is Making Its Self-Driving Cars Smarter. Inc online. Available from:
https://www.inc.com/kevin-j-ryan/how-tesla-is-using-ai-to-make-self-driving-cars-smarter.html Accessed on 14 May 2018.

MARR, B., 2018. The Key Definitions of Artificial Intelligence (AI) That Explain Its Importance. Forbes online. Available from:
https://www.forbes.com/sites/bernardmarr/2018/02/14/the-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance/#3c9f2fdb4f5d Accessed on 14 May 2018.

MARR, B., 2018.  The Amazing Ways Tesla Is Using Artificial Intelligence and Big Data. Forbes online. Available from:
https://www.forbes.com/sites/bernardmarr/2018/01/08/the-amazing-ways-tesla-is-using-artificial-intelligence-and-big-data/#3b7fd4dd4270 Accessed on 15 April 2018.

MENKE, T., 2017. Self-driving Cars: The technology, risks and possibilities. Harvard online. Available from:
http://sitn.hms.harvard.edu/flash/2017/self-driving-cars-technology-risks-possibilities/ Accessed on 15 May 2018.

NVIDIA, 2018. Self- Driving Car. NVIDIA online. Available from:
https://www.nvidia.com/en-us/self-driving-cars/ Accessed on 15 May 2018.

NVIDIA, 2018. Volkswagen and NVIDIA to Infuse AI into Future Vehicle Line-up. NVIDIA online. Available from:
https://nvidianews.nvidia.com/news/volkswagen-and-nvidia-to-infuse-ai-into-future-vehicle-lineup Accessed on 16 May 2018.

NVIDIA, 2018. NVIDIA DRIVE Scalable AI platform for Autonomous Driving –
Self- Driving Car. NVIDIA online. Available from:
https://www.nvidia.com/en-us/self-driving-cars/drive-platform/ Accessed on 16 May 2018.

PLUMMER, L., 2017. Technology: Lidar is set to drastically change the world and how we drive. Here’s how it works. Alphr online. Available from: http://www.alphr.com/technology/1006536/what-is-lidar-how-it-works Accessed on 17 May 2018.

SAGE, A., 2018. Uber and Volkswagen team up with artificial intelligence firm in race to develop self-driving cars. Independent online. Available from:
https://www.independent.co.uk/news/business/news/nvidia-uber-volkswagen-ai-self-driving-cars-artificial-intelligence-graphic-card-a8148076.html Accessed on 17 May 2018.

WOOLLASTON, V., 2017. How do Google’s driverless cars work?. Alphr online. Available from:
http://www.alphr.com/cars/7038/how-do-googles-driverless-cars-work Accessed on 18 May 2018


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