Scroll Top

Perception for Self-driving Cars – What, Why, and How?

Self-driving cars are one of the most significant innovations of our time. They’ve certainly taken the automation game to a whole new level. Thanks to companies like Tesla, the once-conceived-as-a-possibility is now a reality.

That said, two fundamental aspects of self-driving cars are perception and computer vision. These two systems form around 80% of the functions of autonomous vehicles.

In this article, we are discussing how perception works for self-driving cars.

 How Does the Process of Perception Work in Self-Driving Cars?

Perception is a part of deep learning technology that assists with self-driving. It refers to a process through which an autonomous system absorbs information and useful insights from its surroundings.

More profoundly, perception entails the collection of data via high-tech cameras, sensors, etc., and processing of this data to make sense of the environment around the vehicle. Convolutional Neural Network (CNN) is a software system that works as the “brain” for these self-driving vehicles. (Discussed at length later on in this article)

All the operations happen in real-time. The vehicle receives data from sensors, and the installed software enables the core decision-making process. This is how the operation is advocated for its reliability and safety.

Now, self-driving cars use different types of perceptions. For example, environmental perception detects things like road signs and marking. The following section presents a more profound rundown of the same.

What Are the Different Types of Perceptions & Their Uses?

As elucidated above, perception helps self-driving cars to ‘see’ the world around them. It should recognize, analyze, and classify things to make rational decisions.

Here are a few ways self-driving cars use perception.

Environmental Perception

Environmental perception is one of the essential functions of a self-driving car. Without it, a car would be driving with no knowledge of positions, velocity, and obstacles.

LIDARs, cameras, lasers, and radar handle environmental perceptions.

One of the most challenging aspects of environmental perception is that a vehicle is a part of an overall perceived system. Thus, to be entirely safe for humans, self-driving cars should detect movable objects faster.

Then, subsequent algorithms should identify the objects, categorize them, and classify them accordingly. Given that human error is responsible for 94% of fatalities, the goal is to have self-driving cars that are efficient enough to identify threats.

Obstacle Avoidance Perception

Obstacle avoidance is a crucial part of any form of navigation.

With obstacle avoidance perception, the vehicle registers arbitrary shapes of both static and moving objects.

Autonomous vehicles use RADAR and ultrasonic detection to emit signals. Once the signal bounces off an object, it gets transferred to the object detection system. The object detection system provides a relevant input to the obstacle module.

Other than sensors, self-driving cars use cameras to receive information about the environment. A camera can replicate human vision for navigating roads and help in avoiding potential accidents.


A survey shows that only 57% of people are aware and willing to ride self-driving vehicles. Why? Even if self-driving cars provide ultimate safety, people are still being cautious and understandably so.

Understanding human behavior is a highly complex task. It involves not only logic but also emotions that trigger reactions. A full-proof prediction analysis where a car can identify the next course of action taken by the driver is necessary. Once you implement deep learning and the related prediction analysis, a self-driving can be equipped with the knowledge to recognize and behave accordingly.


Autonomous cars use semantic segmentation to evaluate objects like roadways, pavements, dividers, automobiles, etc. Segmentation is a method of comprehension that helps to determine the classification of each pixel of an image.

The vehicle must consider the relevance of each class during the segmentation. Besides, the segmentation should be according to priority to ensure maximum safety. Otherwise, safety might get compromised. For instance, in a self-driving car, a pedestrian image must be more relevant than any other object surrounding the car.


It’s challenging for an autonomous vehicle to be completely reliant on a navigational hardware. Therefore, a self-healing mapping autonomous system assists self-driving vehicles in extending their digital vision and producing a complete road inventory. Integration of this data shows the exact location of all landmarks, traffic signals, lane placements, and a real-time update on road limits.

High-definition maps provide accurate laser-sharp precision of the allotted route. A mapping system is, indeed, a new definition for self-driving cars’ navigation systems.

Perception During Decision-Making

Without decision-making, a self-driving car is one microsecond away from accidents. They work on an intelligent and dynamic system in any unfamiliar environment.

While much depends on sensors, humans may ultimately make unpredictable driving choices. And measuring these isn’t enough to prevent road accidents. Self-driving cars should have every information to take necessary actions in a given situation. While sensors can provide information, it is the deep-learning algorithm that helps with prediction and localization.

Localization comprehends the initial position of the car, and deep reinforcement learning (DRL) helps with possible actions based on the surroundings.

How Does CNN Support Self-Driving Cars?

A massive part of perception in self-driving cars is the use of Convolutional Neural Networks (CNN). CNN is often perceived as a universal non-linear function approximator.

CNN is helpful in generating features from a given image. It captures different patterns even if the layers of the network gradually begin to complicate. For instance, CNN can comprehend arbitrary shapes of leaves on a tree or people in a driving car.

Three major CNN properties make them a primary component of deep-learning algorithms in autonomous vehicles:

  • Spatial sampling
  • Local receptive fields
  • Shared weights

These three components help store representations vital for image classifications, localization, segmentation, mapping, and more.

Concluding Thoughts

Perception is a continuously changing and evolving aspect of self-driving cars. As the technologies and tools become accurate, the algorithm becomes more efficient. Nowadays, companies are striving to decrease the prices of perception sensors and computing devices. This can lead to an exponential growth of the autonomous car market.

In fact, the global self-driving car market is expected to reach $62 billion by 2026. If this prediction holds, the potential for self-driving cars in the forthcoming years will be virtually endless.

Leave a comment