Self-driving vehicles are becoming increasingly popular, but the road to full autonomy has been a long and difficult one. That’s why self-driving startups like Wayve, Waabi, and Autobrains are attempting to bypass the traditional approach with end-to-end AI learning.
By relying on deep learning and artificial intelligence to develop autonomous cars, these companies hope to be able to leapfrog market leaders like Cruise and Waymo.
Wayve’s Approach To Autonomous Driving
The team at Wayve believes that building an autonomous car requires more than just a few sensors, cameras, and other hardware components. Rather, they believe it’s critical to train an AI system so that it can learn from its own mistakes in order to become better over time.
This is what makes their end-to-end AI learning approach different from traditional methods.
To start off its process of developing autonomous vehicles, Wayve built a vehicle simulator called “EVE” (Experiential Virtual Environment) in order to test its algorithms in virtual scenarios. With EVE, the team at Wayve can create realistic simulations of real-world driving environments so that their algorithms can be experienced in various scenarios before being tested on roads.
Waabi: Harnessing AI To Revolutionize Autonomous Vehicles
Waabi is another startup that is developing autonomous vehicles using end-to-end AI learning techniques. While they also use virtual simulators for testing purposes, their approach differs from that of Wayve as they focus on using reinforcement learning systems with visual inputs instead of sensors or other types of hardware components.
Their goal is to use deep reinforcement learning combined with computer vision capabilities in order to enable their autonomous vehicles to learn from their environment in real-time without relying on any preprogrammed behaviors or rulesets.
Waabi believes this approach will revolutionize the way we think about self-driving cars and enable them to adapt far faster than traditional approaches could ever hope for.
Autobrains: Leveraging AI For Autonomous Vehicles
Autobrains also uses an end-to-end AI learning approach for its autonomous vehicle technology development process. They rely heavily on computer vision capabilities combined with deep neural networks and reinforcement learning algorithms for training their autonomous vehicles on how to react in various scenarios without any prior programming knowledge or experience needed from engineers or developers beforehand.
Autobrains also utilizes large datasets for training machine learning models which helps them better understand the vast number of different scenarios a car might encounter while operating autonomously on public roads.
These datasets include images, videos, 3D information from LiDAR sensors, and much more which help them provide robustness and accuracy when developing their self-driving cars.
Will Douglas Heaven & MIT Technology Review’s Role in Driverless Cars
Will Douglas Heaven is the Senior Editor at MIT Technology Review where he focuses on autonomous vehicles and robotics technologies. He has written extensively about driverless car developments over the years and recently was awarded a 2020 Pulitzer Prize nomination for his work covering the potential risks surrounding self-driving vehicles.
Similarly, MIT Technology Review also covers topics related to artificial intelligence (AI) as well as robotics technology within its publications providing readers with insight into current developments within this field as well as future trends likely to emerge over time.
Conclusion: Innovating Autonomous Vehicle Technology with End–to–End AI Learning
Startups like Wayve, Waabi, and Autobrains are pushing the boundaries of autonomy by utilizing end–to–end AI Learning techniques that leverage large datasets combined with computer vision capabilities and powerful deep neural networks in order to quickly create advanced self-driving algorithms capable of navigating complex environments safely in real-time without any prior programming knowledge or experience needed from engineers or developers beforehand.
Ultimately these innovations may very well help us reach full autonomy sooner than many thought possible by helping us bypass the slow progress made by traditional approaches toward achieving true driverless cars for public roads today.
