Do self-driving cars use AI?

Remember the time when we used to fantasise about self-driving automatic vehicles? Well, we are living that future now. Since WWII, computer technology has been one of the few sectors to witness exponential growth. We have witnessed some remarkable achievements in the past few decades. We have come a long way from going paperless to using private satellites for GPS and route mapping. Not to mention our virtual assistant that can use the internet to perform all the basic tasks on voice command. On similar lines, the automobile industry has also been experimenting with the applications of AI in their cars. You, too, can learn possible AI applications by pursuing online

In some sense, we use AI in our driving assistance and road safety mechanisms. However, in a more sophisticated way, we can use AI to drive our cars while enjoying our favorite show or taking a power nap. There are multiple ways self-driving cars work, from full autonomy to partial autonomy to mere assistance. However, companies like Tesla have already rolled out some high-end products that use fully autonomous self-driving mode. Moreover, Tesla collects data from its vehicles to perfect further the AI algorithm, which it introduces via quarterly updates.

Here is an article covering everything you need to know about the use of AI in self-driving cars.

How does AI in self-driving cars work?

Self-driving cars use various devices that coordinate simultaneously to provide a smooth and seamless driving experience. The most important aspect of a self-driving car is implementing the neural network.

This neural network is the key to mapping the best driving mode. A neural network refers to collecting data via cameras mounted on several self-driving cars. This imagery data is stored in the cloud and fed into the machine learning algorithm. It helps in identifying the driving environment and obstacles beforehand.

The neural network data helps the vehicle identify traffic signals, road signs, speed limits, and everyday objects such as trees, dividers, etc. In an ambitious Google project on self-driving named Waymo, the vehicles used a combination of sensors, LIDAR, and several cameras to create a comprehensive image of the environment.

These devices gather data for the machine learning algorithm, which then predicts the movement of non-stationary objects around the vehicle and gives direction to maneuver accordingly. It is pertinent to note that all this takes place in a fraction of seconds, so a high-grade processor is necessary.

Self-driving cars thrive on a bulk of relevant data, so the more these cars drive, the better the algorithm gets. Now, let’s look at a comprehensive set of actions that a self-driving vehicle performs to ensure a safe and comfortable ride:

• First, the user enters the destination on Google Maps, and the car’s advanced software calculates the best route depending on the traffic congestion, road conditions, etc.
• Now, the LIDAR sensor comes into play. It is usually mounted on the car’s roof to create a 60m range map around the car.
• A sensor is also mounted on the vehicle’s left rear wheel, and this sensor must monitor the vehicle’s movement concerning its surroundings.
• The Radio Detection and Ranging systems on the two vertical ends of the vehicle calculate the distance of the obstacle from the vehicle beforehand.
• Now, this is the part where AI sweeps in. The machine learning algorithm mimics human action to control brakes and steering.
• The car’s software now takes in live data from Google Maps regarding upcoming traffic lights, landmarks, etc.
• For legal reasons regarding safety, an override function is always available where the owner can take manual control of the vehicle whenever he likes.

Cars with self-driving features

The self-driving feature is still in its formative years, and we are yet to witness a full-fledged advanced version that won’t require human intervention. The Google Waymo project has attained the highest level of autonomy currently, but it still comes with a human override function, which may not appeal to technology purists. However, partial AI application in cars is quite common today. Here is a list of AI uses in new-generation cars today:

• Adaptive Cruise Control (ACC): ACC is a highly appreciated safety feature in modern In the ACC mode, the vehicles’ sensors calculate the distance of your car concerning the surrounding vehicles. The car software then uses this data to determine the safe distance. If your vehicle goes past the safe distance, the human mimic feature takes over the brakes’ control and stops the vehicle to avoid a collision. ACC is convenient when driving on highways.
• Hands-free steering: Hands-free steering is yet another driver’s assistance feature that helps in minimizing fatigue when driving long distances. Here, the car’s software locks the steering in a center position to prevent the vehicle from swaying away. Not being a full-fledged autonomous feature, the driver must be attentive at all times. However, he can take his hands off the steering for a while and stretch them or grab some snacks.
• Lance-center steering: This convenient feature helps in keeping the vehicle in the designated lane. It can also be treated as a safety feature because it can help prevent high-speed collisions on the highways. Here, the neural network data becomes very useful. The details of lane marking in the machine learning algorithm help the vehicle identify the live surroundings. Hence, if the driver crosses the lane, the vehicle automatically aligns itself by moving the steering in the opposite direction of the movement.

Conclusion

Fully autonomous self-driving cars are still a few years away. However, technological developments in modern vehicles are creating a solid foundation for self-driving cars in the future. Companies are trying to build customers’ trust by exposing them to technology. Additionally, self-driving cars have already found numerous applications in different industries.

Hence, the demand for trained professionals who can develop sophisticated machine learning codes will only increase. Therefore, if you are keen on learning more about making a career in this industry, log in to Great Learning and choose from a wide range of courses to earn an online artificial intelligence degree. Great Learning is home to industry veterans who will help you shape your profile per adequate standards to secure a high-paying job right away.