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Technology Stack Behind a Self-Driving Car: A Survey

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  • July 24, 2021

A self-driving car, also known as an autonomous car, is a vehicle that navigates between sites without the help of a human driver, using sensors, cameras, radar, ultrasound, and artificial intelligence. The car goes to a specified location without human intervention on roads that have not been modified for its usage.

The following are some examples of autonomous cars:

  • Waymo: Google’s self-driving car project, Waymo, is now its enterprise, developing driverless vehicles capable of safely transporting people from point A to B. With over 8 million driverless miles travelled to date, its 360-degree perception system identifies people, other cars, bicycles, road construction, and other hazards from up to 300 yards away.
  • Zoox: While other firms modify existing vehicles with self-driving capability, Zoox is developing their autonomous vehicles from the ground up. The automobiles are being built to be robotic ride-hailing vehicles. A Zoox car is similar to existing transportation services such as Uber or Ola. As a result, a customer could use an app on their smartphone to call a Zoox car for a ride.
  • AutoX: AutoX creates retail-based self-driving cars. With a concentration on online grocery, users can now choose goods through their smartphones and have them delivered.

How do they work?

Sensors, cameras, actuators, radars, sophisticated algorithms, machine learning applications, and powerful processors are used to run the software present in self-driving vehicles. Based on a variety of sensors located throughout the car, driverless cars are able to generate and maintain a map of their surroundings. Radar sensors, on the other hand, track the movement of surrounding cars.

Video cameras are used to detect traffic signals, read road signs, follow other vehicles, and watch pedestrians. Lidar (light detection and ranging) sensors assess distances, see road borders, and recognize road markings by bouncing light pulses off the car’s surroundings. When parking, ultrasonic sensors in the wheels detect the presence of obstacles and other vehicles. Once that is done, sophisticated software analyses all of the sensory data, calculate a course, and delivers instructions to the car’s actuators, which control acceleration, braking, and steering. The software follows traffic regulations and interprets obstacles with the aid of hard-coded rules, predictive modelling, obstacle avoidance algorithms, and object identification.

6 Levels of Vehicle Autonomy

The Society of Automotive Engineers (SAE) classifies driving automation into six categories, ranging from 0 (completely manual) to 5. (fully autonomous). The US Department of Transportation has approved these standards.

The following are the six levels of vehicle autonomy:

Level 0 (No Driving Automation)

Level 0 denotes that the vehicle is operated manually. A large percentage of cars on the road today are level 0 cars. A human driver controls a car in this category at all times. However, mechanisms are in place to assist the driver in driving smoothly.

Level 1 (Driver Assistance)

This is the least advanced degree of automation. The car is equipped with a single automated system for driver assistance, such as steering and accelerating. Adaptive cruise control, which keeps the vehicle at a safe distance behind the next car, is classified as Level 1 since the human driver supervises other aspects of driving, such as steering and braking.

Level 2 (Partial Driving Automation)

Human mistake is the root cause of nearly all vehicle accidents, which can be avoided using Advanced Driver Assistance Systems (ADAS). This is what level 2 is all about. The role of ADAS is to reduce the frequency of automobile accidents to prevent injuries. ADAS can control the vehicle’s steering and acceleration. Since a human sits in the driver’s seat and can take control of the automobile at any time, the automation falls short of self-driving. Level 2 technologies include Super Cruise and Tesla Autopilot.

Level 3 (Conditional Driving Automation)

Level 3 cars are capable of environmental monitoring and can make intelligent judgments for themselves, such as accelerating past a slow-moving vehicle. However, they still require human intervention. If the system cannot complete the task, the driver must stay awake and ready to take charge.

Level 4 (High Driving Automation)

Level 4 vehicles can respond if anything goes wrong or if the system fails, which is an important characteristic that distinguishes Level 4 from Level 3. With regards to this, most of the time, these automobiles don’t require human contact. However, an individual can still manually override the system. Even though these vehicles are capable of self-driving, they can only do so in a restricted region until regulations and infrastructure improve. This is referred to as geofencing. As a result, the majority of Level 4 cars on the road are designed for ridesharing. For example, NAVYA, a French enterprise, is already producing and marketing Level 4 shuttles and taxis in the United States, which run entirely on electric power and have a peak speed of 55 mph.

Level 5 (Full Driving Automation)

Since the human driving duty has been eliminated, Level 5 vehicles will not require any human intervention. They need not have steering wheels or accelerator/braking pedals. Geofencing would not be required for such cars. They will travel anywhere and perform all of the tasks that a trained human driver can.

Benefits of Self-Driving Cars

  • Reduction in Infrastructure Spending: With self-driving vehicles and reduced traffic jams, there will be less need for infrastructure changes because the traffic will handle itself.
  • Energy Conservation: Fuel consumption is a problem for everyone, mainly because gas prices can fluctuate significantly from year to year. The pace at which gasoline is spent can be more readily watched, evaluated, and regulated with autonomous vehicles, dramatically saving system operators money in the long run.
  • Rise in Productivity: Since there will be no traffic and passengers will spend less time actively involved behind the wheel, everyone will have more time to fulfil both career objectives. On their route to work, passengers will be able to read or sleep inside the car. It will minimize fatigue from lengthy travel periods since travellers can relax comfortably within the vehicle, allowing them to get more work done.
  • Reduced Shipping and Delivery time: With autonomous trucks, the conventional cargo trailer that goes from state to state will not have to stop due to tiredness, significantly reducing shipment times.
  • Public Transportation: Driverless cars have the possibility of expanding public transportation choices since almost everyone’s autonomous vehicle could be utilized to transport passengers from one location to another. Instead of parking your car in a garage when you go to work, you could use it to transport other passengers within a specific area and earn money. With this level of autonomous car integration in the transportation system, vehicle owners will gain access to a new revenue source. Since parking spaces will be freed up, they could be modified for other purposes such as the construction of parks or schools.

Some Challenges faced by Self-Driving Cars

  • Lidar: Some of the questions that may occur in an individual’s head are as follows: Would several self-driving cars’ lidar signals interfere with one another if they drove on the same road? Will the frequency range be sufficient to allow the widespread manufacturing of self-driving cars if numerous radio frequencies are available? On top of that, we should also note how lidar is generally expensive.
  • Weather Conditions: What happens when an autonomous vehicle travels in heavy rain? Lane dividers vanish when there is a covering of snow on the road. If the lane markers are covered by water, oil, or ice, how will the cameras and sensors track them?
  • Accident Liability: Who is responsible for accidents caused by self-driving cars?

How ML Algorithms Enabled Self-Driving Cars

There are four types of machine learning algorithms for self-driving cars.

  • Regression Algorithms: Regression techniques are specifically used to anticipate events. The three major types of regression algorithms employed in self-driving cars are neural network regression, Bayesian regression, and decision forest regression. The connection between two or more variables is calculated in regression analysis, and the effects of the variables are compared on different scales. Regression algorithms use repeated characteristics of an environment to create a statistical model of the relationship between a certain image and the position of a specific object inside the image. Through picture sampling, the statistical model can give quick online detection.
  • Classification Algorithms: The pictures obtained by the ADAS contain a great deal of data from the surrounding environment. This data must be filtered to identify photos having a certain category of items. This is where pattern recognition or classification algorithms play its role. These algorithms are intended to eliminate data items that are out of the ordinary. Before categorizing items, it is necessary to recognize patterns in a data collection. The line segments and circular arcs are combined in various ways by pattern recognition algorithms to form the essential attributes for detecting an item. Some of the most often used pattern recognition algorithms in self-driving cars include PCA, Bayes decision rule, and KNN.
  • Cluster Algorithms: Cluster algorithms are very good at determining structure from data points. It is possible for the pictures collected by the ADAS to be blurry, or for classification algorithms to have missed recognizing an item. This generally fails to categorize and report it to the system. Clustering methods are developed via centroid based and hierarchical modelling strategies. All clustering approaches are concerned with utilizing the underlying structures in the data to effectively organize the data into groups with the highest similarity. The two most popular clustering methods for self-driving automobiles are K-means and multi-class neural networks.
  • Decision Matrix Algorithms: They are primarily used for decision-making and are intended to systematically discover, analyze, and evaluate the performance of relationships between attributes and information included within them. AdaBoosting and Gradient Boosting are the most often utilized decision matrix techniques in self-driving vehicles.


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