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Group 2 members:
Phạm Minh Chiến 20194491
Nguyễn Thành Dương 20194531

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- Chiến:
+History of autunomous vehicle
+How to self- driving car work?
- Dương:

+Advantages and disadvantages of autonomous vehicles

+Challenges of AI in Autonomous Vehicles

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The idea of self-driving cars or autonomous vehicles has captured human imagination since the 1930s and with the 2020 slated deadline to put self-driving cars on the streets of major cities in the US and other developed countries, stakes and motivation are definitely high.

It was at Carnegie Mellon University that the first truly autonomous cars appeared (circa 1980); however, it was DARPA's (Defense Advanced Research Projects Agency) self-driving cars competition of 2004, 2005 and 2007 that really kick-started the race to create road-worthy autonomous vehicles . DARPA's challenge to university students and private contractors on developing self-driving cars helped pave the way for a safer and eco-friendly future. This new path is unavoidable and works hand-in-hand to humanity's destiny.

As early as 2014, Tesla Motors had already implemented its Autopilot technology to its electric vehicles and the safety record it has achieved is astounding, to say the least. The cameras and ultrasonic sensors have successfully predicted collisions with up to 76% accuracy and were able to prevent them with over 90% success rate.

Google, Tesla Motors and several other automobile companies envision a future with reduced traffic problems, fewer road accidents and more efficient public and private transport system starting in 2020. Already Tesla and Google have incorporated basic forms of autonomous car capabilities into their vehicles (with the exception of Google only selling its LIDAR technology to car manufacturers and not manufacturing their own cars as Tesla does) which include automatic braking, parking assistance, steering assistance, dynamic radar cruise control, and other features.
The Autonomous Vehicle Concept
The self-driving car initiative was first realized decades before Google started doing technical research on the subject. The first recorded concept of an autonomous car was introduced in the 1939 New York World's Fair in the Futurama section. General Motors created the Futurama exhibit as part of its vision of the future of America in 20 years time. Engineers and futurists included an automated highway system on which the self-driving cars would depend on to get people from one place to another.

Of course, it took over 60 years before robotic vehicles started cruising our streets, but they're not as plentiful as General Motors imagined them to be and they didn't even need to create the automated highway system. However, the goal to develop full-fledged autonomous vehicles is gaining traction with the goal of making driving efficient and safe.

By 1958 - almost two decades since they introduced the concept during the World's Fair in New York - the self-driving car Norman Bel Geddes' created for General Motors was finally realized. It relied on magnetized metal spikes embedded in the roadway and was remotely controlled by a device that guided the car by changing the electromagnetic fields in the spikes to keep the car inside its designated lane.

In 1977 the Tsukuba Mechanical Engineering Lab made some improvements to GM's self-driving car by using cameras linked to a computer, which could guide the car through the road at 20 mph via image data processing.

Ten years later, two of Germany's leading car manufacturers, Daimler and Mercedes Benz, collaborated on a project called VaMoRs. The VaMoRs was a 5-ton Mercedes Benz van equipped with cameras and other sensors, modified to share data with an on-board computer that allowed it to drive the car unassisted. This technology was a big leap from Japan's self-driving car, because it could cruise at 56 mph on any road or highway and not collide or crash with other vehicles or objects.

The improvement of the technologies used in self-driving vehicles is directly proportional to how these types of vehicles perform on the road. Better tech means better autonomous cars, but developers were just starting to scratch the surface.
You Can't Talk About Self-Driving Cars Without Mentioning Elon Musk
The first company that Elon Musk started was Tesla Motors. He intended Tesla to be an all-electric, clean and energy-efficient vehicle manufacturer to kick-start his dream of a green future. The company was incorporated in 2003 and Musk immediately held a press conference stating that he would develop affordable AEVs (autonomous elective vehicles) for the US and the rest of the world in 3 - 5 years time.

The first AEV coupe that Tesla Motors developed was the Tesla Roadster which had a 7,376 ft-lb of torque, can do 0-60 mph in 1.9 seconds and has a top speed of 250+ mph. It can go as far as 620 miles in a single charge and for an AEV released in 2012 with this specs was truly impressive! However, the Roadster cost $200,000 and obviously wasn't feasible for the masses, but even the subsequent Model S still cost twice as much as the $30,000 Tesla sedan Musk promised.

From 2014 onward Tesla started to include the hardware needed for full self-driving capabilities in all of their vehicles even before the software/data was available, and yet Tesla has the best safety record compared to other auto brands in its class. Plus, Tesla's Autopilot® feature in their vehicles has 360-degree coverage! Electronic sensors and cameras literally surround the car on every corner and can recognize cars and pedestrians on the road in various distances.

The narrow forward camera has a maximum viewing distance of 250 meters and it is backed up by the secondary camera/sensor, the main forward camera, which can see up to 150 meters. The wide forward camera scans the peripheral vision of the car up to 200 degrees even though it can only see up to 60 meters out into the road. Finally, the rearward looking cameras cover over 180 degrees of the rear of the vehicle and can see up to 100 meters away.

All these cameras and sensors combine to form a fusion of sensors and eyes of the vehicle making Tesla cars almost military-grade, unmanned navigation systems. But as advanced as this technology may seem, Musk said that it isn't at full self-driving capability yet.

In January of 2019 Musk tweeted that in just 3 - 6 months Tesla motors would slowly depart from the autopilot feature and introduce its first-ever self-driving technology. As early as the year's end, Tesla car owners would be able to safely drive from Los Angeles to New York City without ever touching the vehicle's steering wheel. That's almost 3,000 miles! I will be amazing to see if Musk can deliver on his promise.

But it's not just Tesla Motors that's making such promises to the world, in fact, about half of all the car manufacturers in the world intend to produce autonomous vehicles starting 2020 and onwards. So we might see a significant boost in transportation automation in the next decade or so.
Benefits of Self-Driving Vehicles
Just about every country has a government agency that deals with road safety and licensing drivers as well as the registration of vehicles. In the United States, the Department of Motor Vehicles (DMV) manages the issuance of driver's licenses, registering vehicles, giving out unique plate numbers for traffic monitoring and police surveillance, as well as managing traffic and general road safety for drivers and pedestrians alike. Yet even under the strictest of traffic rules and regulations, there are still over 6 million car accidents that happen in America every year with around 3 million people who suffer injuries related to these accidents, and over 90 people get killed from them on average.

This is where self-driving cars come in handy because scientists predict that they will significantly reduce, if not eradicate, car accidents in the near future.

Here are some of the benefits we can reap from incorporation autonomous vehicles into our everyday lives:

1. Fewer Accidents - computers do not feel fatigued, get hungry, nor get distracted while on the road. In fact, they only exist to drive the car and keep both passengers and pedestrians safe. Their cameras and sensors (which will be greatly improved in the future) are state-of-the-art and can detect and track people and vehicles from every angle of the car. They will be able to detect possible collisions and steer the vehicle safely to avoid accidents. It is estimated that accidents will be reduced by 5% per year in the first decade of implementation alone and could go down as much as 0.15% by 2040.

2. Decrease or Totally Eliminate Traffic Congestion - the future of autonomous vehicles is to have them communicate with each other, thereby reducing traffic congestion and car accidents even more! Driving to your office 10 - 15 miles away will take less than half the time it before self-driving cars were allowed in the streets.

3. Increase Highway Capacity - a recent Columbia University study showed that if there were 100% self-driving cars occupying one lane of a given highway, then vehicle capacity could be increased to as much as 12,000 cars per lane per hour driving at a constant speed of 75 mph. None of our current traffic technology could achieve this feat despite government expenditures reaching several hundred million dollars per annum.

4. Lower Fuel Consumption - assuming that all self-driving cars will be hybrid or all-electric, they could save over 2.9 billion gallons of gasoline per year in the US alone! If electricity was also harvested from clean and renewable energy sources, then that would reduce fuel consumption even further.

5. Enhanced Human Productivity - riding a bus, train or airplane gives passengers more time to do work and other important things versus driving their own car. Now imagine if half of all vehicles were replaced with self-driving cars? Productivity levels would sharply increase and passengers would be able to accomplish more in the same amount of time.

6. Hunting for Car Parking Space Will be Eliminated - self-driving cars would also be able to communicate with parking lots to immediately locate good parking. No more hunting for parking space like the old days which takes at least 5 minutes of circling and wastes precious time.

7. Improved Mobility for Children, the Elderly, And the Disabled - there are three types of people who will have trouble driving cars; a) children, b) the elderly, and c) people with disabilities. Some of these folks completely have to rely on themselves as they no longer have any relatives or next of kin. Giving them the option to own self-driving cars will greatly increase their self-reliance and enable them to function more without needing further assistance.

8. Elimination of Traffic Enforcement Personnel - human traffic enforcers make errors and statistically make traffic congestion even worse. Self-driving cars will eliminate the need for them, making them available for other good services to society.

9. Higher Speed Limits - because self-driving cars will be able to communicate with each other, predict collisions, and adjust the car's speed and direction in a nanosecond, cars will be able to travel faster. Faster travel times could mean never being late for work, school or an important meeting ever again.

10. Lighter, More Versatile Cars - with the efficiency of self-driving cars, soon we won't need to make cars from metals to reinforce them for safety in case of accident. This is because autonomous vehicles will virtually eliminate road accidents. Therefore, cars will be made from cheaper composite materials, and since they'll be lighter, they will travel faster and further on a single charge.

Self-driving cars have been in the making for a long time. The good news is that they're finally here and on the road. Undoubtedly, they will make our transportation safer and more efficient. It's just a matter of time before we're all able to text and ride and leave the driving to the car itself.
Ready to Make History?
If you're ready to make history by developing your own applications for self-driving cars using AI virtual assistants, we invite you to join the Vietnam AI Grand Challenge, a government-backed initiative organized by Kambria. The Grand Challenge will bring together Vietnam's best AI talent and support corporations in Vietnam and globally.

Over 170 team have already registered to compete for their share of $40,000 in prizes. The registration deadline of June 25th is only 2 days away so sign up today!

Hackathon Registration: https://bounty.kambria.io/

Join the discussion group: https://www.facebook.com/groups/VietnamAIGC

Latest updates and events: https://www.facebook.com/VietnamAIGC

For more information: https://www.aigrandchallenge.com/

Kambria is the first blockchain project to build an open innovation platform for frontier technology. We are focusing first on the AI and Robotics vertical with the backing of our well-established robotics company, OhmniLabs. Through existing partnerships with top universities and large international corporations, as well as access to talented developers, we aim to build an ecosystem that can bring the next wave of frontier technology to provide value to billions of people around the world. Kambria - Accelerating Frontier Technology

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The History and Evolution of Self-Driving Cars - Kambria

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The idea of self-driving cars or autonomous vehicles has captured human imagination since the 1930s and with the 2020 slated deadline to put self-driving cars on the streets of major cities in the US and other developed countries, stakes and motivation are definitely high.

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A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings.

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A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings.

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How AI Helps Autonomous Vehicles See Outside the Box - NVIDIA DRIVE Labs Ep. 14

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Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here.A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings.Such fine-grained results can be obtained by segmenting image content with pixel-level accuracy, an approach known as panoptic segmentation.With panoptic segmentation, the image can be accurately parsed for both semantic content (which pixels represent cars vs. pedestrians vs. drivable space), as well as instance content (which pixels represent the same car vs. different car objects).Planning and control modules can use panoptic segmentation results from the perception system to better inform autonomous driving decisions. For example, the detailed object shape and silhouette information helps improve object tracking, resulting in a more accurate input for both steering and acceleration. It can also be used in conjunction with dense (pixel-level) distance-to-object estimation methods to help enable high-resolution 3D depth estimation of a scene.Single DNN ApproachNVIDIA’s approach achieves pixel-level semantic and instance segmentation of a camera image using a single, multi-task learning deep neural network. This approach enables us to train a panoptic segmentation DNN that understands the scene as a whole versus piecewise.It’s also efficient. Just one end-to-end DNN can extract all this rich perception information while achieving per-frame inference times of approximately 5ms on our embedded in-car NVIDIA DRIVE AGX platform. This is much faster than state-of-the-art segmentation methods.DRIVE AGX makes it possible to simultaneously run the panoptic segmentation DNN along with many other DNN networks and perception functions, localization, and planning and control software in real time.

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Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here.A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings.Such fine-grained results can be obtained by segmenting image content with pixel-level accuracy, an approach known as panoptic segmentation.With panoptic segmentation, the image can be accurately parsed for both semantic content (which pixels represent cars vs. pedestrians vs. drivable space), as well as instance content (which pixels represent the same car vs. different car objects).Planning and control modules can use panoptic segmentation results from the perception system to better inform autonomous driving decisions. For example, the detailed object shape and silhouette information helps improve object tracking, resulting in a more accurate input for both steering and acceleration. It can also be used in conjunction with dense (pixel-level) distance-to-object estimation methods to help enable high-resolution 3D depth estimation of a scene.Single DNN ApproachNVIDIA’s approach achieves pixel-level semantic and instance segmentation of a camera image using a single, multi-task learning deep neural network. This approach enables us to train a panoptic segmentation DNN that understands the scene as a whole versus piecewise.It’s also efficient. Just one end-to-end DNN can extract all this rich perception information while achieving per-frame inference times of approximately 5ms on our embedded in-car NVIDIA DRIVE AGX platform. This is much faster than state-of-the-art segmentation methods.DRIVE AGX makes it possible to simultaneously run the panoptic segmentation DNN along with many other DNN networks and perception functions, localization, and planning and control software in real time.

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Editor's note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here.
A self-driving car's view of the world often includes bounding boxes - cars, pedestrians and stop signs neatly encased in red and green rectangles.

In the real world, however, not everything fits in a box.

For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it's helpful for the vehicle's perception software to provide a more detailed understanding of its surroundings.

Such fine-grained results can be obtained by segmenting image content with pixel-level accuracy, an approach known as panoptic segmentation.

With panoptic segmentation, the image can be accurately parsed for both semantic content (which pixels represent cars vs. pedestrians vs. drivable space), as well as instance content (which pixels represent the same car vs. different car objects).

Planning and control modules can use panoptic segmentation results from the perception system to better inform autonomous driving decisions. For example, the detailed object shape and silhouette information helps improve object tracking, resulting in a more accurate input for both steering and acceleration. It can also be used in conjunction with dense (pixel-level) distance-to-object estimation methods to help enable high-resolution 3D depth estimation of a scene.

Single DNN Approach

NVIDIA's approach achieves pixel-level semantic and instance segmentation of a camera image using a single, multi-task learning deep neural network. This approach enables us to train a panoptic segmentation DNN that understands the scene as a whole versus piecewise.

It's also efficient. Just one end-to-end DNN can extract all this rich perception information while achieving per-frame inference times of approximately 5ms on our embedded in-car NVIDIA DRIVE AGX platform. This is much faster than state-of-the-art segmentation methods.

DRIVE AGX makes it possible to simultaneously run the panoptic segmentation DNN along with many other DNN networks and perception functions, localization, and planning and control software in real time.

As shown above, the DNN is able to segment a scene into several object classes, as well as detect different instances of these object classes, as shown with the unique colors and numbers in the bottom panel.

On-Point Training and Perception

The rich pixel-level information provided by each frame also reduces training data volume requirements. Specifically, because more pixels per training image represent useful information, the DNN is able to learn using fewer training images.

Moreover, based on the pixel-level detection results and post-processing, we're also able to compute the bounding box for each object detection. All the perception advantages offered by pixel-level segmentations require processing, which is why we developed the powerful NVIDIA DRIVE AGX Xavier.

As a result, the pixel-level details panoptic segmentation provides make it possible to better perceive the visual richness of the real world in support of safe and reliable autonomous driving.

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Panoptic Segmentation Helps Autonomous Vehicles See Outside the Box | NVIDIA Blog

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Editor's note: This is the latest post in our NVIDIA DRIVE Labs series , which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here .

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A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence ( AI) to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human intervention to a predetermined destination over roads that have not been adapted for its use.

Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen and Volvo. Google's test involved a fleet of self-driving cars -- including Toyota Prii and an Audi TT -- navigating over 140,000 miles of California streets and highways.

Levels of autonomy in self-driving cars

The U.S. National Highway Traffic Safety Administration ( NHTSA) lays out six levels of automation, beginning with zero, where humans do the driving, through driver assistance technologies up to fully autonomous cars. Here are the five levels that follow zero automation:

Level 1: Advanced driver assistance system (ADAS) aid the human driver with either steering, braking or accelerating, though not simultaneously. ADAS includes rearview cameras and features like a vibrating seat warning to alert drivers when they drift out of the traveling lane.

Level 2: An ADAS that can steer and either brake or accelerate simultaneously while the driver remains fully aware behind the wheel and continues to act as the driver.

Level 3: An automated driving system (ADS) can perform all driving tasks under certain circumstances, such as parking the car. In these circumstances, the human driver must be ready to re-take control and is still required to be the main driver of the vehicle.

Level 4: An ADS is able to perform all driving tasks and monitor the driving environment in certain circumstances. In those circumstances, the ADS is reliable enough that the human driver needn't pay attention.

Level 5: The vehicle's ADS acts as a virtual chauffeur and does all the driving in all circumstances. The human occupants are passengers and are never expected to drive the vehicle.

As of 2018, car makers have reached Level 3. Self-driving vehicles are at least a few years off, because manufacturers must clear a variety of technological milestones and a number of important issues must be addressed before autonomous vehicles can be purchased and used on public roads in the United States, according to the NHTSA.

How self-driving cars work

AI technologies power self-driving car systems. Developers of self-driving cars use vast amounts of data from image recognition systems, along with machine learning and neural networks, to build systems that can drive autonomously.

The neural networks identify patterns in the data, which is fed to the machine learning algorithms. That data includes images from cameras on self-driving cars from which the neural network learns to identify as traffic lights, trees, curbs, pedestrians, street signs and other parts of any given driving environment.

For example, Google's self-driving car project, called Waymo, uses a mix of sensors, Lidar (light detection and ranging -- a technology similar to radar), and cameras and combines all of the data those systems generate to identify everything around the vehicle and predict what those objects might do next. This happens in fractions of a second. The system learns more as it drives, so maturity is important with these systems.

How Google Waymo vehicles work:

The driver (or passenger) sets a destination. The car's software calculates a route.

A rotating, roof-mounted Lidar sensor monitors a 60-meter range around the car and creates a dynamic 3D map of the car's current environment.

A sensor on the left rear wheel monitors sideways movement to detect the car's position relative to the 3D map.

Radar systems in the front and rear bumpers calculate distances to obstacles.

AI software in the car is connected to all the sensors and collects input from Google Street View and video cameras inside the car.

The AI simulates human perceptual and decision-making processes and controls actions in driver-control systems such as steering and brakes.

The car's software consults Google Maps for advance notice of things like landmarks and traffic signs and lights.

An override function is available to allow a human to take control of the vehicle.

The pros and cons of autonomous cars

The top benefit touted by autonomous vehicle proponents is safety. A U.S. Department of Transportation and NHTSA statistical projection of traffic fatalities for 2017 estimated that 37,150 people died in motor vehicle traffic crashes that year. The NHTSA estimates that 94% of serious crashes are due to human error or poor choices, such as drunk or distracted driving. Autonomous cars remove those risk factors from the equation -- though self-driving cars are still vulnerable to other factors, such as mechanical issues, that cause crashes.

If autonomous cars can significantly reduce the number of crashes, the economic benefits could be enormous. Injuries impact economic activity, including $57.6 billion in lost workplace productivity, and $594 billion due to loss of life and decreased quality of life due to injuries, according to the NHTSA.

In theory, if the roads were mostly occupied by autonomous cars, traffic would flow smoothly and there would be less traffic congestion. In cars that are fully automated, the occupants could do productive activities while commuting to work. People who aren't able to drive due to physical limitations could find new independence through autonomous vehicles and would have the opportunity to work in fields that require driving.

Autonomous trucks have been tested in the U.S. and Europe to allow drivers to use autopilot over long distances, freeing the driver to rest or complete tasks and improving driver safety and fuel efficiency. This initiative, called truck platooning, is powered by adaptive cruise control ( ACC), collision avoidance systems and vehicle-to-vehicle communications for cooperative adaptive cruise control (CACC).

The downsides of autonomous vehicle technology could be that riding in a vehicle without a driver behind the steering wheel may be unnerving, at least at first. But as self-driving capabilities become commonplace, human drivers may become overly reliant on the autopilot technology and leave their safety in the hands of automation, even when they should act as backup drivers in case of software failures or mechanical issues.

In one example from March 2018, Tesla's Model X SUV was on autopilot when it crashed into a highway lane divider. The driver's hands were not on the wheel, despite visual warnings and an audible warning to put his hands back on the steering wheel, according to the company.

Autonomous car safety and challenges

Autonomous cars must learn to identify countless objects in the vehicle's path, from branches and litter to animals and people. Other challenges on the road are tunnels that interfere with Global Positioning Systems ( GPS), construction projects that cause lane changes, or complex decisions, like where to stop to allow emergency vehicles to pass.

The systems need to make instantaneous decisions on when to slow down, swerve or continue acceleration normally. This is a continuing challenge for developers and there are reports of self-driving cars hesitating and swerving unnecessarily when objects are detected in or near the roadways.

This problem was clear in a fatal accident in March 2018 which involved an autonomous car operated by Uber. The company reported that the vehicle's software identified a pedestrian but deemed it a false positive and failed to swerve to avoid hitting her. This crash caused Toyota to temporarily cease its testing of self-driving cars on public roads, but its testing will continue elsewhere. The Toyota Research Institute is constructing a test facility on a 60-acre site in Michigan to further develop automated vehicle technology.

With crashes also comes the question of liability, and lawmakers have yet to define who is liable when an autonomous car is involved in an accident. There are also serious concerns that the software used to operate autonomous vehicles can be hacked, and automotive companies are working to address cybersecurity risks.

Car makers are subject to Federal Motor Vehicle Safety Standards, and the NHTSA reports that more work must be done for vehicles to meet those standards.

The road to driverless cars

The path toward self-driving cars began with incremental automation features for safety and convenience before the year 2000, with cruise control and antilock brakes. After the turn of the millennium, advanced safety features including electronic stability control, blind spot detection and collision and lane shift warnings became available in vehicles. Between 2010 and 2016, advanced driver assistance capabilities such as rearview video cameras, automatic emergency brakes and lane centering assistance emerged, according to the NHTSA.

Since 2016, automation has moved toward partial autonomy, with features that help drivers stay in their lane, along with adaptive cruise control technology, and the ability to self-park.

Fully automated vehicles are not publicly available yet and may not be for many years. In the U.S., the NHTSA provides federal guidance for introducing automated driving systems onto public roads and as autonomous car technologies advance, so will the department's guidance.

Self-driving cars are not yet legal on most roads. In June 2011, Nevada became the first jurisdiction in the world to allow driverless cars to be tested on public roadways; California, Florida, Ohio and Washington, D.C., have followed in the years since.

The history of driverless cars goes back much further than that. Leonardo da Vinci designed the first prototype around 1478. Leonardo's car was designed as a self-propelled robot powered by springs, with programmable steering and the ability to run preset courses.

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What is self-driving car (autonomous car or driverless car)? - Definition from WhatIs.com

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This definition explains the meaning of self-driving car, also known as autonomous car or driverless car, and how this type of vehicle uses artificial intelligence technologies to move from one destination to another without the aid of a human driver.

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Article By : Anton Hristozov

Autonomous vehicles (AVs) are starting to become a real possibility in some parts of industry. Agriculture, transportation and military are some of the examples. The day when we are going to see autonomous vehicles in everyday life for the regular consumer is quickly approaching. Many of the operations that vehicles have to perform are based on sensor information and some artificial intelligence (AI) algorithms. Vehicles need to collect data, plan their trajectory and execute the trajectory. These tasks, especially the last two require non-traditional programming approaches and rely on machine learning techniques, which are part of AI. This two-part series illustrates the AI applications that make autonomous vehicles a reality, presenting their challenges and accomplishments. Part 1 offered an analysis of AI and its applications in AVs. This article explores the algorithms and challenges of AI in AVs.
3. AI algorithms used in autonomous 3.1. Route Planning and Control Algorithms vehicles
Traditional algorithms from computer science that are heuristic in nature can be used for this task. These are algorithms like Bellman-Ford and Dijkstra's algorithm (Bugala, 2018). For these algorithms to work we need to have localization of the vehicle during the whole time. Localization is accomplished through sensors such as GPS as well as simultaneous localization and mapping (SLAM) techniques.
3.2. Object Detection Algorithms
SLAM is used when there is no GPS availability such as underground or enclosed spaces for example. SLAM generates a map of the environment and at the same time estimates the state of a vehicle (Cadena et al., 2016). The map is composed of landmarks or obstacles in order to represent the environment. SLAM is used in applications where the map is not available and needs to be created. It uses sensors and special algorithms that create models of the data in order to produce the map.

Object detection is one of the most important tasks that AI has to handle in a moving vehicle. These algorithms are an area of active research and they rely on different sensors. Object detection can be based on cameras or lidars, radars and other type of sensors. The algorithms used are normally deep learning algorithms which use some type of a neural network to do the job (Redmon et al., 2016).

One requirement for this task is that it needs to be fast. The reason is because there is a succession of images that need to be processed as the vehicle moves.
3.3. Decision Making Algorithms
Some of the latest techniques here are based on the use of convolutional neural networks (CNN). These are R-CNN, Fast R-CNN and You Only Look Once (Yolo) methods (Redmon et al., 2016). RCNN first finds regions that contain potential objects in an image and then tries to analyze each region. This makes R-CNN somewhat slow and that is why there have been fast R-CNN methods developed and the Yolo method. Yolo works simultaneously to find the regions and to classify the objects in the regions by using a single convolutional neural network.This makes Yolo very fast compared to the other methods. In addition Yolo is able to see the entire image and does not suffer from the issues in R-CNN like mistaking background images for objects.

Decision making determines the actions of the vehicle based on information from sensors. A vehicle constantly makes decision, based on its policy and the environment. The algorithms used for decision making are the following (Bugala, 2018):
4. Challenges of AI in Autonomous Vehicles

Decision Trees

Support Vector Machine (SVM) Regression

Deep Reinforcement Learning

Some of the challenges of using artificial intelligence algorithms for autonomous vehicles are the same challenges that are universal for many other AI applications. The autonomous vehicles domain introduces some additional, unique challenges. These are concepts like real time, safety and machine ethics. The following subsections describe these areas in detail.

Real time systems are specific class of embedded systems which have the characteristics to produce outputs or reactions within certain time restrictions. They have to be deterministic and minimalist in their design, so that they can always meet the expected real time behavior. For this to happen they often use special real time operating systems (RTOS) or bare metal executives that interact directly with the hardware, avoiding interpreted languages and dynamic memory allocation. In some cases real time systems intentionally use a subset of a programming language in order to guarantee speed and determinism.
4.2 Computational complexity
The artificial intelligence solutions are normally on the opposite side of the spectrum - using higher level programming languages, full operating system dependencies, techniques like dynamic memory allocation and garbage collection. This creates a challenge in making real time guarantees with these systems.
4.3 Black box behavior
Another aspect of these systems is whether they are centralized or distributed as described in (Carmody, Thomas, 2019). Centralized systems are easier to architect, but rely heavily on internal communications and a powerful central processor. Distributed system use dedicated CPUs that can handle different subsystems and sensors thus alleviating the need for a complex and powerful central CPU. Distributed architecture allow for systems with lower power consumption. They are also more flexible and can be cheaper.

Artificial intelligence algorithms, especially deep learning, require special hardware solutions due to the amount of data and the complexity of computations. Hardware, like graphic processing units (GPU) or tensor processing units (TPU) can be highly optimized for fast parallel computation. The speed comes at a price of higher energy consumption and higher cost. Even with using specialized hardware there is still no certainty that a particular algorithms will be able to reach a solution in real time constraints. Therefore the selection of algorithms with regards to their complexity and CPU demands is a serious factor in real time systems. Usage of CPU computation and memory are the measures that can help determine if an algorithm is suitable for use in a typical CPU used in modern vehicles.

AI algorithms have been criticized for being harder to analyze than standard computer algorithms. This comes from the fact that AI algorithms have a higher level of complexity and rely on a lot of data. A complex neural network can perform several AI tasks without understanding the process it controls.
4.4 Accuracy and Reliability
In fact a deep neural network is approximating a function and this is part of its nature and explains why it can be so successful. The deep neural networks can have tens of thousands of nodes that can be trained to reach certain state. In addition they can have many hidden layers and many inputs and outputs. All this leads to problems when a system crashes and there is the need to do forensics analysis and find out the precise cause for the crash. This is an active area of research and successes in this area will solve many legal and technical issues related to using AI.

One idea is to use hybrid solutions which combine AI with traditional control algorithms (Abduljabbar and Dia, 2019). This issue is particularly important for accident prediction and handling (Narayanan, 2019). The decision history of why one decision was made versus another has to be justified by the AI algorithm and this is hard or impossible if the algorithm can be only analyzed and viewed as a black box.

The computer vision applications used in autonomous vehicles may not be ready for prime time. The reason is that they can work in pristine conditions, but can fail with some even small disturbances at the sensor inputs. The training of AI algorithms happens slowly with training data that has certain characteristics.

Changing the data can make the classification and prediction algorithms change their behavior dramatically with catastrophic results. For example a person who carries a large bag, may not be perceived as a person by the vehicle.
4.6 Security and AI
In addition it is hard to predict what can happen with arbitrary data that can enter the system. This issue has been exploited by attackers who were able to fool deep learning algorithms by data in images that is not seen by humans, but makes the neural network stop classifying properly. There are clearly a lot of improvements needed to improve the reliability and accuracy of the machine learning algorithms.

The complexity of AI can lead to problems with safety. A more complicated system is one that is more difficult to develop, test and deploy. This is only part of the problem. Another reality is that new safety standards are now emerging and not yet adopted by the transportation industry (Carmody, Thomas, 2019).

What makes things even more complicated is that analysis and verification of AI systems is something that is still in its early stages and may not be tractable, because of their complexity. In general, formal verification is hard, even for traditional software systems and it is even harder for AI based software. The problem with creating robust, verified and validated AI solutions is something that we have to solve in general, not only for autonomous vehicles, but for all AI applications.
4.7 Ethics and AI
AI systems are so central to the autonomous vehicle that their security is directly related to robustness of the whole system. Machine learning systems are susceptible to attacks and there have been many examples of such cases (Newman, 2019).
5. Conclusion
The biggest threats are when an adversary can manipulate data that comes from sensors to the vehicle thus making the system make incorrect decisions. This behavior can be improved by training systems with data that has some adversarial characteristics. An experiment with a deep learning model for recognizing road signs showed that "adding just a few black and white stickers to a stop sign tricked the algorithm into thinking it was a 45mph speed limit sign." (Newman, 2019,p.17).
- Anton Hristozov graduated from the University of Pittsburgh with a Masters in Telecommunications and Information Science. He is currently pursuing a doctoral degree in Technology at Purdue University.
A big threat for affecting how AI operates in an autonomous vehicle is through perturbing sensor operation, which leads to changes in the sensor data stream and thus can completely confuse the AI algorithms. Solutions like hybrid systems, fuzzy logic and traditional control systems based on models to complement AI are some ways that researchers are trying to explore. The stakes are high, since AI can be found in some vehicle subsystems already and will be in every vehicle in the near future.

At this point, the ethical aspects of AI implementations in autonomous and semi-autonomous vehicles are not mature and are not significantly developed. Ethical values are a human quality, which is hard to formalize and implement in machines. In other words deciding what is right or wrong is kind of nebulous to a machine (Narayanan, 2019). Full development of machine ethics may be decades away. This is an area of active research which is a cross discipline between human psychology, machine learning and social policy. Possible some regulations can help in this direction while the technology becomes more mature.

AI solutions are everywhere today. They are part of devices like Alexa and Google Home, part of grass cutting robots and part of autonomous vehicles we expect to drive soon. The challenges and achievements of AI are common for many industries, but in the transportation industry and especially with autonomous vehicles we have extra challenges related to security and safety. Part of it is because of the complexity of the task to create a self driving vehicle and part of it is because of the level of development of the field of AI and its suitability to solve complex problems like object detection, route planning and real time decision making. Many of these obstacles will be resolved as we move forward towards more and more penetration of autonomous solutions in certain sectors of the economy. Maybe the right question to ask for this industry is "Are we going to get there safely?" and not just "Are we there yet ?".

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AI algorithms and challenges for autonomous vehicles - EDN Asia

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Article By : Anton Hristozov Autonomous vehicles (AVs) are starting to become a real possibility in some parts of industry. Agriculture, transportation and military are some of the examples. The day when we are going to see autonomous vehicles in everyday life for the regular consumer is quickly approaching.

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Advantages and disadvantages of autonomous vehicles

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Actualidad sobre Mobility, New Gen, Self-driving vehicles.

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