Algorithms are key for Autonomous Vehicles

Algorithms are playing a key role in the journey of the autonomous vehicle —they truly are the biggest secret behind the self-driving cars and trucks. Algorithms are part of our everyday lives nowadays in several products and services, but they need to learn what humans already know to become more intelligent for ensuring an efficient and safe performance in autonomous vehicles.

Self-driving cars and trucks are a huge topic for both Artificial Intelligence (AI) and the automotive industries because the newest models are more computers-on-wheels than vehicles. Algorithms used on those vehicles need to be trained to perform in the real world as they are raw theoretical potential written by engineers. Algorithms must learn what the outside world really is since they can avoid hitting a pedestrian or a car on the street, except they first need to be taught what they are. This seems counterintuitive for humans due is within our nature, but is extremely challenging for a computer.

In addition to the massive variety of possible hazard scenarios and malfunctions that could unfold while driving and impact the way that self-driving vehicles function, unsupervised or supervised algorithms (depending on how they do Machine Learning) must identify a road sign, determine what that sign says, and then be able to react accordingly. They must render the surrounding environment and forecast the changes around by Detecting, Identifying/Recognizing, Locating, and Predicting the movement.

Carmakers and OEMs are adding computing capabilities to provide 360-degree vision and process the captured high-resolution images, incorporating sensor data processing in embedded ECUs (Electronic Control Units). Engineers are using all of those data entries to develop autonomous vehicles combining them with Human-in-the-Loop (HITL) technologies to collect, clean, and label data that is used for Machine Learning and Neural Networks to train and deploy the algorithms in the real world.

Algorithms applications can go from evaluating the driver condition or its speech, to gesture recognition and language translation, even scenario classification using data fusion from several external and internal sensing technologies —like cameras, LiDAR and Radar, Vehicle-to-Everything (V2X) communications or the Internet of Things (IoT).

Algorithms are key for autonomous vehicles by becoming more intelligent, highly accurate (regarding robustness and efficiency) and more importantly its safety.