The Technology of Self-Driving Cars is not a secret – So what does Drive these Vehicles?

Vehicles will experience significant changes in the future decades, specifically in terms of how they are operated. There will be a shift away from human-driven cars toward self-driving vehicles, which will have a major impact on people’s lives. But, have you ever wondered what is the technology of self-driving cars? How is that possible that these vehicles can drive safely without human intervention?  

Well, autonomous technology is a complex system consisting of software, cameras, sensors, radar, and laser beams that work together and navigate a self-driving car. In this article, we’ll explain how the self-driving car works by providing details of Self-Driving Cars’ key elements and technology behind that drives these “machines” forward.

What is the self-driving car?

A self-driving car, also known as a driverless car or autonomous car, is a vehicle that drives itself between destinations. It’s feasible with the use of sensors, cameras, radar, and Artificial intelligence (AI).

There are six levels of self-driving cars. To be fully autonomous, self-driving vehicles must be able to get to a predetermined location without human involvement. Roads can’t be modified. A self-driving car can travel everywhere a human driver can go and perform everything that a human driver can do.

What are the Key Elements of Self-Driving Cars Technology?

Self-driving vehicles are made up of five fundamental elements:

Key Elements of Self-Driving Cars

Computer vision – is the process through which we use camera pictures to determine how the world around us looks like.

Sensor fusion – is the process to combine data from various sensors such as Cameras and Lidars to get a more detailed understanding of our environment.

Localization – after we’ve developed a thorough understanding of the world around us, we use localization to determine our precise location within it.

Path planning – once we’ve determined our precise location within the world and what the world looks like, we use path planning to plot a course through it that will get us to our destination.

Control – represents the last phase. This is the process where we move the steering wheel, press the accelerator and brake pedals to execute the route that we’ve created during path planning.

So, those are the five fundamental components of self-driving vehicles. For a better understanding of how the self-driving car works, we’d want to go through each of them with you in further detail.

Computer Vision

The use of computer vision is the first element in the workflow for autonomous vehicles. Using data from its several cameras, the car can identify objects among its surrounding environment and then combine these elements with information obtained from radar and lidar.

Computer vision is required for a wide range of critical functions such as 3D maps creation, lane finding, road curvature estimates, obstacle detection, and classification with road signs recognition and classification.

3D Map Creation – The vehicle’s cameras can snap real-time pictures. These images are used to construct 3D maps. Autonomous cars may use 3D maps to navigate safely and can choose other routes in case of an impending collision. 

Lane finding – Cutting lanes may be disastrous for self-driving cars. Segmentation methods are used to recognize lane lines and remain in the designated lane during self-driving. It can also identify road turns and curves, keeping passengers secure.

Obstacle detection and classification – This technology can categorize and detect objects. The vehicle can utilize Lidar sensors and cameras to determine distance. The data may be used to detect traffic signals, cars, and people using 3D maps. These high-tech cars use real-time data processing to make decisions.

As an example, to identify the road’s lane, cameras are used to find colors, edges, and gradients. All self-driving cars are equipped with several cameras strategically placed throughout the vehicle. Then the deep neural network is learned to create bounding boxes around other cars. Deep learning, also known as deep neural networks, is a unique approach for computers to learn about vehicles and other objects by providing them with masses of data. 

Considering cameras are one of the main devices that the self-driving vehicle uses to understand its surroundings, It is common that computer vision is known as “perception”.  Consequently, computer vision technology will help self-driving cars prevent accidents.

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Sensor Fusion

Now it’s time to fuse camera pictures with other sensors data. In this phase, sensor fusion is coming into play as one of the key elements in the Technology of Self-Driving Cars.

So, after we’ve determined what the environment looks like through camera pictures, the next step is to enhance that data with other sensors such as Lidar, Radar, or Ultrasonic transducers.

Lidar Filter, segment, and cluster raw lidar data to detect other cars on the road. It illuminates a target using pulsed laser light. Then analyses the reflected pulses to calculate the source distance to the target. Lidar is commonly used to create high-definition world maps due to its excellent 3D geometry accuracy. Lidars are often installed on various areas of Autonomous Cars, such as the top, side, and front, for various functions.

Radars have been used in the automotive industry to enable ADAS features like autonomous emergency braking or adaptive cruise control. Radars can accurately detect an object’s range and radial velocity by emitting an electromagnetic wave. They are very effective at identifying metallic objects, but they can also detect non-metallic items like pedestrians and trees from a short distance.

Ultrasonic transducers measure the time between transmitting an ultrasonic signal and receiving its echo. That information is then used to calculate the distance to an object. Ultrasonic transducers are often used for Autonomy Car localization and navigation.

Sensor fusion delivers a more comprehensive and exact picture of the environment than a single source could. Sensor fusion, a field of machine learning and signal processing that focuses on perception combines data from several sensors and databases. This is to provide higher-quality information that enables an autonomous system to make better, safer decisions. 

Sensor fusion systems integrate data from cameras, LiDAR, radar, effectively enhancing data from one sensor with data from another. The massive quantity of sensor data that an autonomous system must analyze to make fast decisions is handled by highly efficient software and specialized hardware known as accelerators.

As an example, the series of lasers are doing a 360-degree scan of the environment. These data in combination with camera images give us precise measurements of the distance between us and other vehicles.

Sensor Fusion is a broad topic and an important element of a deep understanding of how the self-driving car works. For those interested in mastering it and pursuing a career as a Sensor Fusion Engineer, the following Udacity Nanodegree is great to enroll in:

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Localization

Once we know what the environment looks like around us and we have measured it, the next phase is to determine our position in that environment. Nowadays, when we use GPS on daily basis, that seems to be straightforward. But not for self-driving cars. GPS is accurate to one or two meters. In self-driving cars, while determining location we need to assure single-digit centimeter accuracy. Therefore we must use far more advanced mathematical algorithms supported with high-definition maps to precisely localize our car in its environment.

To achieve such accuracy, self-driving cars use a system that compares what sensors detect to what is displayed on a map. Vehicle sensors can calculate the distance between a vehicle and solid objects like trees, poles, road signs, and barriers. In the vehicle co-ordinate frame, we estimate the distances and directions between these static objects. The car sensors may have picked up a landmark that is also on the map. We correlate our sensors’ landmark observations with the positions of those landmarks on the map to estimate the vehicle’s position on the map. The vehicle has its own set of coordinates, as does the map.

The self-driving car software must convert sensor measurements from the vehicle coordinate frame to the map coordinate system and vice versa. The algorithm must then determine the vehicle’s location on the map with less than 10cm accuracy.

Particle filter is one of the solutions in the Technology of Self-Driving Cars and is used to reach such a level of accuracy.

While moving, the car measures its distance from landmarks. In reality, the landmarks may be traffic lights, road signs, or any other objects around.

Simultaneously, it initiates a number of particles, each indicating a potential vehicle position. The principle is to estimate the probability of each particle being the real vehicle based on the measurements from the actual car and the particles. Irrelevant particles will be systematically filtered away as the car moves.

To find the orientation, we let the vehicle drive further. Finally, just the most accurately located particle remains which indicate the car’s exact position.

Path Planning

After we’ve figured out the exact location, the next phase is to determine how to get to our destination. We do it by using path planning. To plan the vehicle’s path, we use HD mapping, localization, and prediction. The system needs to consider a broad variety of factors to determine the path. The goal is to generate the safest, most convenient, and most cost-effective routes from point A to point B.

Just after the track planner develops a path, the behavioral layer analyses the surrounding area and generates the most relevant motion specification. The next step is for the motion planner to provide a realistic driving mode that conforms to the specified requirement. Lastly, the feedback system continuously adapts the mode to rectify faults and overcome road obstructions.

Finding a route is challenging as the vehicle must recognize and avoid obstacles. Path planning in autonomous vehicles is based on two primary components: behavioral prediction of moving objects and behavioral planning for the self-driving car.

Behavioral prediction of moving objects

Moving objects are tracked using multiple-model route planning algorithms. That predicts the movement of all dynamic objects in space and corridor. This information is used to predict each object’s trajectory in real-time. Path planning algorithms analyze various potential movements for each object and correlate them with the latest road condition. The expected trajectory is then built using high-probability movements. Finally, the path planning system considers the most relevant car reaction.

Behavioral planning for the self-driving car

Vehicle behavior planning includes driving efficiency and safety balanced with comfort.

As a result, the two most important components of vehicle behavior planning are lane classification for driving efficiency and feasibility assessment to drive to this lane safely:

  • Driving efficiency is selecting the optimal lane to travel in the fastest time possible
  • Comfort means feasible and safe driving to that lane

Control

This is the final element in the Technology of Self-Driving Cars. In simple words, control is used to execute the trajectory created during path planning. The most fundamental control inputs for a vehicle are steering, acceleration, and braking. Trajectories are sent to the controller as a series of waypoints. The controller’s job is to maneuver the car throughout these waypoints using the control inputs.

The controller must be accurate. It should not deviate from the intended trajectory even if driving conditions are changing with sharp turns or wet road surfaces. This is very significant in terms of safety.

It is also important to drive comfortably. If a vehicle operates unpredictably, passengers will not want to drive in again. To maintain a comfortable level of control, actuation must be continuous, which implies avoiding rapid acceleration, turning, or braking.

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How to become Self Driving Cars Engineer?

A growing number of experts will be needed as the Technology of Self-Driving Cars continues to evolve in the future years. Self-driving vehicle engineers have the potential for a long-term impact within the industry. Therefore individuals who want to follow this as a career path should ensure they have a complete set of skills with an in-depth understanding of how the self-driving car works.

Udacity’s self-driving vehicle nanodegree programs are currently one of the best options if you’d like to be well prepared for challenges in this exciting industry.

This is the first online learning platform to provide comprehensive solutions. To be successful in the Autonomous Vehicles industry, their nanodegrees cover a large portion of the necessary skills. The Autonomous Vehicles program was launched by Udacity in 2017 as the first institution in the world to educate self-driving car technology. There have already been thousands of students who have signed up for these classes. In 2021 Udacity updated Self Driving Cars Engineer Nanodegree to its newest version with upgraded projects, new simulators, and the latest practical knowledge.

Their core Self-Driving Cars programs are:

No matter if you’re a youngster or an adult. If you’re a total beginner or have some experience with self-driving cars related challenges. Based on your existing knowledge and expertise, you may choose the appropriate learning path to become a Self-Driving Cars Engineer.

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Udacity is an example of an online platform that truly benefits students. Their Nanodegrees are of the best quality focused on Job Ready Skills. They cover many of the industry’s most in-demand skills, such as programming, app development, UX design, cloud computing, autonomous systems, and business skills.

Conclusion

In this article, we’ve explained the basics of the core technology of self-driving cars and the fundamental principle of how the self-driving car works. Those passionate individuals who’d like to continue with more in-depth practical knowledge should consider further development with Udacity Self Driving Cars Nanodegrees. By choosing an appropriate learning path, these programs will benefit beginners as well as more advanced students

Autonomous vehicles need a diverse set of technology, including specialized sensors, cameras, and software. This sophisticated technology of Self Driving Cars is required to ensure the safe and reliable functioning of autonomous vehicles. As companies work to progress autonomous car technology, will largely depend on engineers’ expertise to develop, build and test it assuring safe and comfortable driverless travel.

In short, there was no better time to begin a career in the Autonomous Vehicles industry and start working for one of the top Self Driving Cars companies.

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